analysis of hydraulic tomography using temporal moments of drawdown recovery data received 2 june 2005 revised 30 september 2005 accepted 21 october 2005 published 1 february 2006 1 transient hydraulic tomography tht is a potentially cost effective and highresolution technique for mapping spatial distributions of the hydraulic conductivity and specific storage in aquifers interpretation of abundant well hydrographs of a tht survey however is a computational challenge we take on this challenge by developing an estimation approach that utilizes the zeroth and first temporal moments of well hydrographs instead of drawdown itself the governing equations for the temporal moments are poisson s equations these equations demand less computational resources as opposed to the parabolic equation that governs drawdown evolution likewise the adjoint equations for evaluating sensitivities of the moments for parameter estimation also take the same forms therefore a temporal moment approach is expected to expedite the interpretation of tht surveys on the basis of this premise we extend our sequential successive linear estimator to use the zeroth moment and characteristic time of the drawdown recovery data generated by tht surveys we subsequently investigate computational efficiency and accuracy of the moment approach results of the investigation show that the temporal moment approach yields results similar to those from the approach that uses transient heads but at significantly less computational costs limitations using temporal moments are discussed subsequently

general framework for dynamic substructuring history review and classification of techniques four decades after the development of the first dynamic substructuring techniques there is a necessity to classify the different methods in a general framework that outlines the relations between them in this paper a certain vision on substructuring methods is proposed by recalling important historical milestones that allow us to understand substructuring as a domain decomposition concept thereafter based on the dual and primal assembly of substructures a general framework for the classification of the methods is presented this framework allows us to indicate how the various classes of methods proposed along the years can be derived from a clear mathematical description of substructured problems current bottlenecks in experimental dynamic substructuring as well as solutions found in literature will also be briefly discussed

dynamical model for competing opinions we propose an opinion model based on agents located at the vertices of a regular lattice each agent has an independent opinion among an arbitrary but fixed number of choices and its own degree of conviction the latter changes every time two agents which have different opinions interact with each other the dynamics leads to size distributions of clusters made up of agents which have the same opinion and are located at contiguous spatial positions which follow a power law as long as the range of the interaction between the agents is not too short i e the system self organizes into a critical state short range interactions lead to an exponential cutoff in the size distribution and to spatial correlations which cause agents which have the same opinion to be closely grouped when the diversity of opinions is restricted to two a nonconsensus dynamic is observed with unequal population fractions whereas consensus is reached if the agents are also allowed to interact with those located far from them the individual agents convictions the preestablished interaction range and the locality of the interaction between a pair of agents their neighborhood has no effect on the interaction are the main characteristics which distinguish our model from previous ones

structural damage assessment using a generalized minimum rank perturbation theory recently the authors proposed computationally attractive algorithms to determine the location and extent of structural damage for undamped structures assuming damage results in a localized change in stiffness properties the algorithms make use of a finite element model and a subset of measured eigenvalues and eigenvectors the developed theories approach the damage location and extent problem in a decoupled fashion first a theory is developed to determine the location of structural damage with location determined a damage extent theory is then developed the damage extent algorithm is a minimum rank perturbation which is consistent with the effects of many classes of structural damage on a finite element model in this work the concept of the minimum rank perturbation theory mrpt is adopted to determine the damage extent on the mass properties of an undamped structure in addition the mrpt is extended to the case of proportionall y damped structures for proportionally damped structures the mrpt is used to find the damage extent in any two of the three structural property matrices mass damping or stiffness finally illustrative case studies using both numerical and actual experimental data are presented he advent of the space shuttle has prompted considerable attention to the design and control of large space structures due to the large size and complexity of envisioned structures as well as the use of advanced materials to reduce structural weight it may become necessary to develop a structural health monitoring system to detect and locate structural damage as it occurs from experience gained in the machinery health monitoring field one would expect the vibration signature of the structure either frequency response functions and or modal parameters to provide useful information in determining the location and extent of structural damage assume that a refined finite element model fem of the structure has been developed before damage has occurred by refined we mean that the measured and analytical modal properties are in agreement next assume that at a later date some form of structural damage has occurred if significant the damage will result in a change in the structures modal parameters the question is can the discrepancy between the original fem modal properties and postdamage modal properties be used to ascertain structural damage most prior work in damage detection has used the general framework of fem refinement system identification in the development of damage assessment algorithms the motivation behind the development of fem refinement techniques is based on the need to validate engineering fems before their acceptance as the basis for final design analysis the standard problem has been to seek a refined fem that is as close to the original fem and whose modal properties are in agreement with those that are measured subject to various constraints such as symmetry and sparsity preservation a considerable amount of work in this area has been

impact of sampling volume on the probability density function of steady state concentration 1 in recent years statistical theory has been used to compute the ensemble mean and variance of solute concentration in aquifer formations with second order stationary velocity fields the merit of accurately estimating the mean and variance of concentration however remains unclear without knowing the shape of the probability density function pdf in a setup where a conservative solute is continuously injected into a domain the concentration is bounded between zero and the concentration value in the injected solution at small travel distances close to the fringe of the plume an observation point may fall into the plume or outside so that the statistical concentration distribution clusters at the two limiting values obviously this results in non gaussian pdf s of concentration with increasing travel distance the lateral plume boundaries are smoothed resulting in increased probability of intermediate concentrations likewise averaging the concentration in a larger sampling volume as typically done in field measurements leads to higher probabilities of intermediate concentrations we present semianalytical results of concentration pdf s for measurements with point like or larger support volumes based on stochastic theory applied to stationary media to this end we employ a reversed auxiliary transport problem in which we use analytical expressions for first and second central spatial lateral moments with an assumed gaussian pdf for the uncertainty of the first lateral moment and gauss like shapes in individual cross sections the resulting concentration pdf can be reasonably fitted by beta distributions the results are compared to monte carlo simulations of flow and steady state transport in 3 d heterogeneous domains in both methods the shape of the concentration pdf changes with distance to the contaminant source near the source the distribution is multimodal whereas it becomes a unimodal beta distribution far away from the contaminant source the semianalytical and empirical pdf s differ slightly which we contribute to the numerical artifacts in the monte carlo simulations but also to hard assumptions made in the semianalytical approach our results imply that geostatistical techniques for interpolation and other statistical inferences based on gaussian distributions such as kriging and cokriging may be feasible only far away from the contaminant source for calculations near the source the beta like distribution of concentration should be accounted for

eigenstructure assignment approach for structural damage detection in this work a methodology for incorporating measured modal data into an existing refined finite element model is examined with the objective of detecting and locating structural damage the algorithm is based on the partial inverse problem in that only partial spectral information is required the technique utilizes a symmetric eigenstructure assignment algorithm to perform the partial spectral assignment algorithms to enhance mode shape assignability and to preserve sparsity in the damaged fem are developed the sparsity preservation is of particular importance when considering damage detection in trusslike structures several examples are presented to highlight the key points made within the paper

a test to determine the multivariate normality of a data set a test is described for multivariate normality that is useful in pattern recognition the test is based on the friedman rafsky 1979 multivariate extension of the wald wolfowitz runs test the test data are combined with a multivariate swarm of points following the normal distribution generated with mean vector and covariance matrix estimated from the test data the minimal spanning tree of this resultant ensemble of points is computed and the count of the interpopulation edges in the minimal spanning tree is used as a test statistic the simulation studied both the null case of the test and one simple deviation from normality two conclusions are made from this study first the test can be conservatively applied by using the asymptotic normality of the test statistic even for small sample sizes second the power of the test appears reasonable especially in high dimensions monte carlo experiments were performed to determine if the test is reliable in high dimensions with moderate sample size the method is compared to other such tests available in the literature

sequential ensemble based optimal design for parameter estimation the ensemble kalman filter enkf has been widely used in parameter estimation for hydrological models the focus of most previous studies was to develop more efficient analysis estimation algorithms on the other hand it is intuitively understandable that a well designed sampling data collection strategy should provide more informative measurements and subsequently improve the parameter estimation in this work a sequential ensemble based optimal design seod method coupled with enkf information theory and sequential optimal design is proposed to improve the performance of parameter estimation based on the first and second order statistics different information metrics including the shannon entropy difference sd degrees of freedom for signal dfs and relative entropy re are used to design the optimal sampling strategy respectively the effectiveness of the proposed method is illustrated by synthetic one and two dimensional unsaturated flow case studies it is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies optimal sampling designs based on various information metrics perform similarly in our cases the effect of ensemble size on the optimal design is also investigated overall larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy although the proposed method is applied to unsaturated flow problems in this study it can be equally applied in any other hydrological problems this article is protected by copyright all rights reserved

updating finite element dynamic models using an element by element sensitivity methodology a sensitivity based methodology for improving the finite element model of a given structure using test modal data and a few sensors is presented the proposed method searches for both the location and sources of the mass and stiffness errors and does not interfere with the theory behind the finite element model while correcting these errors the updating algorithm is derived from the unconstrained minimization of the squared 2 norms of the modal dynamic residuals via an iterative two step staggered procedure at each iteration the measured mode shapes are first expanded assuming that the model is error free then the model parameters are corrected assuming that the expanded mode shapes are exact the numerical algorithm is implemented in an element by element fashion and is capable of zooming on the detected error locations several simulation examples which demonstrate the potential of the proposed methodology are discussed i introduction e ven though the field of finite element analysis of structural dynamic problems has witnessed tremendous progress in the last two decades greater confidence is still placed in experimental data therefore the finite element model of a given structure is often updated to reflect the results of a particular experiment usually the computed eigenmodes are compared with the measured frequencies and mode shapes if both sets are not in agreement the finite element model is refined via a two step updating procedure which 1 locates the errors and 2 corrects them clearly the first step is the most challenging of the two once the location of the errors is known it is relatively easy to correct them especially if the error sources can be identified however locating and identifying these errors can be a difficult task for the following reasons in general only a few experimental modes are available and these may be contaminated with random and systematic measuring errors moreover only a subset of the degrees of freedom dof in the finite element model can be monitored and as for practical and economical reasons only a few sensors can be utilized

nested sampling algorithm for subsurface flow model selection uncertainty quantification and nonlinear calibration 1 calibration of subsurface flow models is an essential step for managing ground water aquifers designing of contaminant remediation plans and maximizing recovery from hydrocarbon reservoirs we investigate an efficient sampling algorithm known as nested sampling ns which can simultaneously sample the posterior distribution for uncertainty quantification and estimate the bayesian evidence for model selection model selection statistics such as the bayesian evidence are needed to choose or assign different weights to different models of different levels of complexities in this work we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems the estimated bayesian evidence by the ns algorithm is used to weight different parameterizations of the subsurface flow models prior model selection the results of the numerical evaluation implicitly enforced occam s razor where simpler models with fewer number of parameters are favored over complex models the proper level of model complexity was automatically determined based on the information content of the calibration data and the data mismatch of the calibrated model

quasi linear geostatistical theory for inversing a quasi linear theory is presented for the geostatistical solution to the inverse problem the archetypal problem is to estimate the log transmissivity function from observations of head and log transmissivity at selected locations the unknown is parameterized as a realization of a random field and the estimation problem is solved in two phases structural analysis where the random field is characterized followed by estimation of the log transmissivity conditional on all observations the proposed method generalizes the linear approach of kitanidis and vomvoris 1983 the generalized method is superior to the linear method in cases of large contrast in formation properties but informative measurements i e there are enough observations that the variance of estimation error of the log transmissivity is small the methodology deals rigorously with unknown drift coefficients and yields estimates of covariance parameters that are unbiased and grid independent the applicability of the methodology is demonstrated through an example that includes structural analysis determination of best estimates and conditional simulations

real time updating of structural mechanics models using kalman filtering modified constitutive relation error and proper generalized decomposition the paper aims at proposing a new strategy for real time identification or updating of structural mechanics models defined as dynamical systems the main idea is to introduce the modified constitutive relation error concept which is a practical tool that enables to efficiently solve identification problems with highly corrupted data into the kalman filtering which is a classical framework for data assimilation furthermore a pgd based model reduction method is performed in order to optimize capabilities of the online updating strategy performances of the proposed approach in terms of robustness gain and computational cost reduction are illustrated on several unsteady thermal applications

reduced bases for model updating in structural dynamics based on constitutive relation error abstract this paper deals with the application of a model reduction method to the updating of models of industrial structures with many degrees of freedom the updating method is based on the concept of constitutive relation error this is an iterative method in which each iteration consists of a first step in which the most important errors in the model are localized and a second step in which these errors are corrected the reduction method follows a classical approach in which we introduce a truncated modal basis to which the static responses associated with different excitations are added the efficiency of the method is illustrated on one example of a finite element model containing 10 000 degrees of freedom in the first part we update the mass and stiffness properties of the model based on eigenmodes and eigenfrequencies in the second part the damping properties are updated based on the frequency response functions of the structure

comparison of deterministic and probabilistic approaches to identify the dynamic moving load and damages of a reinforced concrete beam two classical civil engineering inverse problems are considered the first deals with the determination of dynamic moving loads applied to a reinforced concrete beam the second one corresponds to the monitoring and the damage assessment the concrete damage due to overloading is modeled by a loss of the concrete young modulus whereas the steel bar damage due to corrosion effects is modeled by a reduction of the steel bar cross section to identify the loading and damage parameters deterministic and probabilistic model updating techniques are applied and compared in the deterministic approach a gradient descent technique based on the adjoint framework is used to minimize the data misfit functional with a tikhonov regularization term then a regularization by a means of bayes s rule is considered in a probabilistic approach the estimation is of the minimum variance type achieved with the help of the transformed ensemble kalman filter

cluster size entropy in the axelrod model of social influence small world networks and mass media we study the axelrod s cultural adaptation model using the concept of cluster size entropy s c which gives information on the variability of the cultural cluster size present in the system using networks of different topologies from regular to random we find that the critical point of the well known nonequilibrium monocultural multicultural order disorder transition of the axelrod model is given by the maximum of the s c q distributions the width of the cluster entropy distributions can be used to qualitatively determine whether the transition is first or second order by scaling the cluster entropy distributions we were able to obtain a relationship between the critical cultural trait q c and the number f of cultural features in two dimensional regular networks we also analyze the effect of the mass media external field on social systems within the axelrod model in a square network we find a partially ordered phase whose largest cultural cluster is not aligned with the external field in contrast with a recent suggestion that this type of phase cannot be formed in regular networks we draw a q b phase diagram for the axelrod model in regular networks

stochastic analysis of boundaries effects on head spatial variability in heterogeneous aquifers 2 impervious boundary the spatial variability of the head h in a two dimensional flow in the vicinity of an impervious boundary is investigated this study is a continuation of a previous one rubin and dagan 1988 which dealt with the influence of a given head boundary by representing h and the log transmissivity y as space random function and by solving the flow equation first order closed form analytical expressions for the head variogram and the head log transmissivity y covariance are developed these expressions offer the means to evaluate the influence of the boundary on the joint y h random field the assumption of unbounded domain is examined and it is found to be a good approximation at distances larger than three log transmissivity integral scales from the boundary by using conditional probabilities a simple method to estimate the statistical moments close to the boundary is presented it requires the knowledge of the moments of y and h in the unbounded domain only the method is compared with the exact analytical solution showing favorable results

a robust and efficient approach to causal inference based on sparse sufficient dimension reduction a fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given measured confounding variables this assumption of no missing confounders is plausible if a large number of baseline covariates are included in the analysis as we often have no prior knowledge of which variables can be important confounders thus estimation of treatment effects with a large number of covariates has received considerable attention in recent years most existing methods require specifying certain parametric models involving the outcome treatment and confounding variables and employ a variable selection procedure to identify confounders however selection of a proper set of confounders depends on correct specification of the working models the bias due to model misspecification and incorrect selection of confounding variables can yield misleading results we propose a robust and efficient approach for inference about the average treatment effect via a flexible modeling strategy incorporating penalized variable selection specifically we consider an estimator constructed based on an efficient influence function that involves a propensity score and an outcome regression we then propose a new sparse sufficient dimension reduction method to estimate these two functions without making restrictive parametric modeling assumptions the proposed estimator of the average treatment effect is asymptotically normal and semiparametrically efficient without the need for variable selection consistency the proposed methods are illustrated via simulation studies and a biomedical application

modified constitutive relation error an identification framework dealing with the reliability of information abstract this paper proposes an identification strategy based on the principles of the modified constitutive relation error m cre the basic idea is to construct mechanical fields and material parameters that are a trade off between all the available information but with no further hypothesis the paper focuses on the identification of elastic properties from dic digital image correlation data in statics and the dealing of various kinds of boundary conditions unknown or known is especially discussed we study our methodology on numerical examples in both homogeneous and heterogeneous isotropic elasticity but a target application is the orthotopic behavior of aeronautic composites

effect of external fields in axelrod s model of social dynamics the study of the effects of spatially uniform fields on the steady state properties of axelrod s model has yielded plenty of counterintuitive results here we reexamine the impact of this type of field for a selection of parameters such that the field free steady state of the model is heterogeneous or multicultural analyses of both one and two dimensional versions of axelrod s model indicate that the steady state remains heterogeneous regardless of the value of the field strength turning on the field leads to a discontinuous decrease on the number of cultural domains which we argue is due to the instability of zero field heterogeneous absorbing configurations we find however that spatially nonuniform fields that implement a consensus rule among the neighborhood of the agents enforce homogenization although the overall effects of the fields are essentially the same irrespective of the dimensionality of the model we argue that the dimensionality has a significant impact on the stability of the field free homogeneous steady state

sliced inverse regression for dimension reduction abstract modern advances in computing power have greatly widened scientists scope in gathering and investigating information from many variables information which might have been ignored in the past yet to effectively scan a large pool of variables is not an easy task although our ability to interact with data has been much enhanced by recent innovations in dynamic graphics in this article we propose a novel data analytic tool sliced inverse regression sir for reducing the dimension of the input variable x without going through any parametric or nonparametric model fitting process this method explores the simplicity of the inverse view of regression that is instead of regressing the univariate output variable y against the multivariate x we regress x against y forward regression and inverse regression are connected by a theorem that motivates this method the theoretical properties of sir are investigated under a model of the form y f 1 x k x e where the k s are the unknown

bayesian geostatistical design task driven optimal site investigation when the geostatistical model is uncertain 1 geostatistical optimal design optimizes subsurface exploration for maximum information toward task specific prediction goals until recently most geostatistical design studies have assumed that the geostatistical description i e the mean trends covariance models and their parameters is given a priori this contradicts as emphasized by rubin and dagan 1987a the fact that only few or even no data at all offer support for such assumptions prior to the bulk of exploration effort we believe that geostatistical design should 1 avoid unjustified a priori assumptions on the geostatistical description 2 instead reduce geostatistical model uncertainty as secondary design objective 3 rate this secondary objective optimal for the overall prediction goal and 4 be robust even under inaccurate geostatistical assumptions bayesian geostatistical design follows these guidelines by considering uncertain covariance model parameters we transfer this concept from kriging like applications to geostatistical inverse problems we also deem it inappropriate to consider parametric uncertainty only within a single covariance model the matern family of covariance functions has an additional shape parameter controlling model shape by a parameter converts covariance model selection to parameter identification and resembles bayesian model averaging over a continuous spectrum of covariance models this is appealing since it generalizes bayesian model averaging from a finite number to an infinite number of models we illustrate how our approach fulfills the above four guidelines in a series of synthetic test cases the underlying scenarios are to minimize the prediction variance of 1 contaminant concentration or 2 arrival time at an ecologically sensitive location by optimal placement of hydraulic head and log conductivity measurements results highlight how both the impact of geostatistical model uncertainty and the sampling network design vary according to the choice of objective function

stochastic analysis of head spatial variability in bounded rectangular heterogeneous aquifers groundwater flow through bounded rectangular aquifers is analyzed in a stochastic framework new analytical expressions of the head covariance and variance and the log transmissivity head cross covariance are derived for the simplified first order problem effects of different boundary conditions the aquifer size and the log transmissivity correlation scale upon head moments are studied the results are compared to analytical half plane solutions demonstrating significant differences in several cases finally the problem is solved numerically using the monte carlo simulation method the realizations of the log transmissivity field are generated in two different ways through comparisons with the analytical results the accuracy of the numerical methods is shown to be excellent

selecting a ritz basis for the reanalysis of the frequency response functions of modified structures abstract the exact reanalysis of a structure is time consuming and when repeated reanalyses are needed it is often preferable to use an approximate ritz technique this approximate method consists in expressing the new frequency response function as a linear combination of vectors in a truncated modal basis one notices that even though the convergence to the exact frequency response function is monotonic it is generally irregular when the number of vectors introduced in the modal sub basis increases a solution to this consists of choosing the additional eigenvectors of the basis in such a way so as to give a best representation of the new frequency response function this choice is not an easy task particularly when the parametric modifications are very local and of large amplitudes this article describes an original approximate reanalysis technique for accurately evaluating frequency response of a modified structure it introduces new concepts for evaluating the static contribution of neglected eigenvectors resulting in a set of additional vectors completing the original ritz basis this method will be appreciated by designers working on the optimization of a prototype it can also be used during the iterations of a model updating procedure based on measured frequency response functions

stochastic analysis of boundaries effects on head spatial variability in heterogeneous aquifers 1 constant head boundary the spatial variability of log transmissivity y and head h in aquifers assumes a major role in determining the solutions of the direct flow problem as well as of the inverse problem investigation of the joint y h variability has been cast in the past in numerical and analytic frames analytic solutions for the y h statistical moments in two dimensions assumed the flow to occur in an unbounded domain in this study exact analytical expressions for the y h moments are derived which account for the presence of a given head boundary in a half plane this result is achieved by a first order approximation and for an exponential y covariance the result offers the opportunity to examine the effects of boundaries on the semivariogram of h and on the y h cross covariance the assumption of unbounded domain is examined and shown to be accurate at distances larger than two y integral scales from the boundary by using conditional probabilities a general and simple method to estimate the moments close to the boundary is presented it requires the knowledge of the moments of y and h in the unbounded domain only the method is compared with the exact analytic solution showing excellent results

two dimensional characterization of hydraulic heterogeneity by multiple pumping tests 1 the conventional analysis of pumping tests by type curve methods is based on the assumption of a homogeneous aquifer applying these techniques to pumping test data from real heterogeneous aquifers leads to estimates of the hydraulic parameters that depend on the choice of the pumping and observation well positions in this paper we test whether these values may be viewed as pseudo local values of transmissivity and storativity which can be interpolated by kriging we compare such estimates to those obtained by geostatistical inverse modeling where heterogeneity is assumed in all stages of estimation we use drawdown data from multiple pumping tests conducted at the test site in krauthausen germany the geometric mean values of transmissivity and storativity determined by type curve analysis are very close to those obtained by geostatistical inversion but the conventional approach failed to resolve the spatial variability of transmissivity in contrast the estimate from geostatistical inversion reveals more structure this indicates that the estimates of the type curve approaches can not be treated as pseudo local values concerning storativity both analysis methods show strong fluctuations because the variability of all terms making up the storativity is small we believe that the estimated variability of storativity is biased we examine the influence of measurement error on estimating structural parameters of covariance functions in the inversion we obtain larger correlation lengths and smaller prior variances if we trust the measured data less

geostatistical interpolation using copulas 1 in many applications of geostatistical methods the dependence structure of the investigated parameter is described solely with the variogram or covariance functions which are susceptible to measurement anomalies and implies the assumption of gaussian dependence moreover the kriging variance respects only observation density data geometry and the variogram model to address these problems we borrow the idea from copulas to depict the dependence structure without the influence of the marginal distribution the methodology and basic hypotheses for application of copulas as geostatistical methods are discussed and the gaussian copula as well as a non gaussian copula are used in this paper copula parameters are estimated using a division of the observations into multipoint subsets and a subsequent maximization of the corresponding likelihood function the interpolation is carried out with two different copulas where the expected and median values are calculated from the copulas conditioned with the nearby observations the full conditional copulas provide the estimation distributions for the unobserved locations and can be used to define confidence intervals which depend on both the observation geometry and values observations of a large scale groundwater quality measurement network in baden wurttemberg are used to demonstrate the methodology five groundwater quality parameters chloride nitrate ph sulfate and dissolved oxygen are investigated all five parameters show non gaussian dependence the copula based interpolation results of the five parameters are compared to the results of conventional ordinary and indicator kriging different statistical measures including mean squared error relative differences and probability scores are used to compare cross validation and split sampling results of the interpolation methods the non gaussian copulas give better results than the geostatistical interpolations validation of the confidence intervals shows that they are more realistic than the estimation variances obtained by ordinary kriging

message survival and decision dynamics in a class of reactive complex systems subject to external fields abstract in this study the dynamics of decisions in complex networks subject to external fields are studied within a markov process framework using nonlinear dynamical systems theory a mathematical discrete time model is derived using a set of basic assumptions regarding the convincement mechanisms associated with two competing opinions the model is analyzed with respect to the multiplicity of critical points and the stability of extinction states sufficient conditions for extinction are derived in terms of the convincement probabilities and the maximum eigenvalues of the associated connectivity matrices the influences of exogenous e g mass media based effects on decision behavior are analyzed qualitatively the current analysis predicts i the presence of fixed point multiplicity with a maximum number of four different fixed points multi stability and sensitivity with respect to the process parameters and ii the bounded but significant impact of exogenous perturbations on the decision behavior these predictions were verified using a set of numerical simulations based on a scale free network topology

global interactions information flow and chaos synchronization we investigate the relationship between the emergence of chaos synchronization and the information flow in dynamical systems possessing homogeneous or heterogeneous global interactions whose origin can be external driven systems or internal autonomous systems by employing general models of coupled chaotic maps for such systems we show that the presence of a homogeneous global field either external or internal for all times is not indispensable for achieving complete or generalized synchronization in a system of chaotic elements complete synchronization can also appear with heterogeneous global fields it does not requires the simultaneous sharing of the field by all the elements in a system we use the normalized mutual information and the information transfer between global and local variables to characterize complete and generalized synchronization we show that these information measures can characterize both types of synchronized states and also allow us to discern the origin of a global interaction field a synchronization state emerges when a sufficient amount of information provided by a field is shared by all the elements in the system on the average over long times thus the maximum value of the top down information transfer can be used as a predictor of synchronization in a system as a parameter is varied

efficient estimation of average treatment effects using the estimated propensity score we are interested in estimating the average effect of a binary treatment on a scalar outcome if assignment to the treatment is exogenous or unconfounded that is independent of the potential outcomes given covariates biases associated with simple treatmentcontrol average comparisons can be removed by adjusting for differences in the covariates rosenbaum and rubin 1983 show that adjusting solely for differences between treated and control units in the propensity score removes all biases associated with differences in covariates although adjusting for differences in the propensity score removes all the bias this can come at the expense of efficiency as shown by hahn 1998 heckman ichimura and todd 1998 and robins mark and newey 1992 we show that weighting by the inverse of a nonparametric estimate of the propensity score rather than the true propensity score leads to an efficient estimate of the average treatment effect we provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score

improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data considerable recent interest has focused on doubly robust estimators for a population mean response in the presence of incomplete data which involve models for both the propensity score and the regression of outcome on covariates the usual doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for some observations we propose alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods even with some estimated propensity scores close to zero

best unbiased ensemble linearization and the quasi linear kalman ensemble generator 1 linearized representations of the stochastic groundwater flow and transport equations have been heavily used in hydrogeology e g for geostatistical inversion or generating conditional realizations the respective linearizations are commonly defined via jacobians numerical sensitivity matrices this study will show that jacobian based linearizations are biased with nonminimal error variance in the ensemble sense an alternative linearization approach will be derived from the principles of unbiasedness and minimum error variance the resulting paradigm prefers empirical cross covariances from monte carlo analyses over those from linearized error propagation and points toward methods like ensemble kalman filters enkfs unlike conditional simulation in geostatistical applications enkfs condition transient state variables rather than geostatistical parameter fields recently modifications toward geostatistical applications have been tested and used this study completes the transformation of enkfs to geostatistical conditioning tools on the basis of best unbiased ensemble linearization to distinguish it from the original enkf the new method is called the kalman ensemble generator keg the new context of best unbiased ensemble linearization provides an additional theoretical foundation to enkf like methods such as the keg like enkfs and derivates the keg is optimal for gaussian variables toward increased robustness and accuracy in non gaussian and nonlinear cases sequential updating acceptance rejection sampling successive linearization and a levenberg marquardt formalism are added state variables are updated through simulation with updated parameters always guaranteeing the physicalness of all state variables the keg combines the computational efficiency of linearized methods with the robustness of enkfs and accuracy of expensive realization based methods while drawing on the advantages of conditional simulation over conditional estimation such as adequate representation of solute dispersion as proof of concept a large scale numerical test case with 200 synthetic sets of flow and tracer data is conducted and analyzed

sparse reduced rank regression for simultaneous dimension reduction and variable selection the reduced rank regression is an effective method in predicting multiple response variables from the same set of predictor variables it reduces the number of model parameters and takes advantage of interrelations between the response variables and hence improves predictive accuracy we propose to select relevant variables for reduced rank regression by using a sparsity inducing penalty we apply a group lasso type penalty that treats each row of the matrix of the regression coefficients as a group and show that this penalty satisfies certain desirable invariance properties we develop two numerical algorithms to solve the penalized regression problem and establish the asymptotic consistency of the proposed method in particular the manifold structure of the reduced rank regression coefficient matrix is considered and studied in our theoretical analysis in our simulation study and real data analysis the new method is compared with several existing variable selection methods for multivariate regression

effects of the underlying topology on perturbation spreading in the axelrod model for cultural dissemination abstract we study the effects of the underlying topologies on a single feature perturbation imposed to the axelrod model of consensus formation from the numerical simulations we show that there are successive updates which are similar to avalanches in many self organized criticality systems when a perturbation is imposed we find that the distribution of avalanche size satisfies the finite size scaling fss ansatz on two dimensional lattices and random networks however on scale free networks with the degree exponent 3 we show that the avalanche size distribution does not satisfy the fss ansatz the results indicate that the disordered configurations on two dimensional lattices or on random networks are still stable against the perturbation in the limit n network size however on scale free networks with 3 the perturbation always drives the disordered phase into an ordered phase the possible relationship between the properties of phase transition of the axelrod model and the avalanche distribution is also discussed

updating structural dynamic models with emphasis on the damping properties the control of complex structural models is a e eld of growing interest the problem studied herein concerns damping updating using experimental frequency response functions although there are several methods improving structural dynamic models only a few of them deal specie cally with damping improvements the method introduced is built on mechanical bases using an error measure on the constitutive relations the tuning strategy uses an iterative process each iteration consisting of two steps the e rst one is the localization of the erroneous regions the second step is the correction of the parameters belonging to these regions examples illustrate the sensitivity of the error to the damping defects and its effectiveness for updating damped structures

the central role of the propensity score in observational studies for causal effects abstract the results of observational studies are often disputed because of nonrandom treatment assignment for example patients at greater risk may be overrepresented in some treatment group this paper discusses the central role of propensity scores and balancing scores in the analysis of observational studies the propensity score is the estimated conditional probability of assignment to a particular treatment given a vector of observed covariates both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates applications include matched sampling on the univariate propensity score which is equal percent bias reducing under more general conditions than required for discriminant matching multivariate adjustment by subclassification on balancing scores where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations and visual representation of multivariate adjustment by a two dimensional plot author

sequential imputations and bayesian missing data problems abstract for missing data problems tanner and wong have described a data augmentation procedure that approximates the actual posterior distribution of the parameter vector by a mixture of complete data posteriors their method of constructing the complete data sets is closely related to the gibbs sampler both required iterations and similar to the em algorithm convergence can be slow we introduce in this article an alternative procedure that involves imputing the missing data sequentially and computing appropriate importance sampling weights in many applications this new procedure works very well without the need for iterations sensitivity analysis influence analysis and updating with new data can be performed cheaply bayesian prediction and model selection can also be incorporated examples taken from a wide range of applications are used for illustration

probability density functions of hydraulic head and velocity in three dimensional heterogeneous porous media 1 in this study we assess probability density functions of hydraulic heads and specific discharges in three dimensional bounded heterogeneous porous media by monte carlo mc simulation we discuss their empirical shapes and demonstrate that the intuitive use of obvious information on boundedness leads to parametric distribution functions which fit surprisingly well on the basis of statistical moments of hydraulic heads and velocities up to fourth order we discuss the spatial dependence of the empirical distributions and their dependence on the variance of log conductivity comparison of the first and second central moment to the results from classical numerical first order second moment fosm analysis reveals that fosm predicts these moments surprisingly close for hydraulic heads on the basis of this fact we demonstrate that fitting the chosen parametric distributions for hydraulic heads to fosm moments is promising for the sake of estimating exceedance probabilities our mc scenarios vary in variance of log conductivity 0 125 to 5 0 in the type of multivariate dependence in correlation scale and types of boundary conditions our study illustrates that in contrast to the common assumption fosm is a reasonable choice for evaluating multivariate and univariate moments for heads if used in conjunction with additional information on distribution shapes in the absence of utilizable additional information we demonstrate that second moment methods are mostly inadequate for assessing distributions accurately significant deviations from gaussian distributions occurred for discharge components even at a variance of log conductivity as low as 0 125 and we found that the distributions of transverse discharge components are extremely fat tailed the observed non gaussianity questions the results of approximate approaches in solute flux and dispersion studies where velocity fields are assumed to be multi gaussian and then directly represented by or generated from their covariances the main implication is to apply more accurate schemes such as exact non local methods extensive mc or higher order stochastic galerkin approaches and to include higher order moments at least if no additional assumptions on the shape of distributions are available or justifiable

large scale inverse model analyses employing fast randomized data reduction when the number of observations is large it is computationally challenging to apply classical inverse modeling techniques we have developed a new computationally efficient technique for solving inverse problems with a large number of observations e g on the order of 107 or greater our method which we call the randomized geostatistical approach rga is built upon the principal component geostatistical approach pcga we employ a data reduction technique combined with the pcga to improve the computational efficiency and reduce the memory usage specifically we employ a randomized numerical linear algebra technique based on a so called sketching matrix to effectively reduce the dimension of the observations without losing the information content needed for the inverse analysis in this way the computational and memory costs for rga scale with the information content rather than the size of the calibration data our algorithm is coded in julia and implemented in the mads open source high performance computational framework http mads lanl gov we apply our new inverse modeling method to invert for a synthetic transmissivity field compared to a standard geostatistical approach ga our method is more efficient when the number of observations is large most importantly our method is capable of solving larger inverse problems than the standard ga and pcga approaches therefore our new model inversion method is a powerful tool for solving large scale inverse problems the method can be applied in any field and is not limited to hydrogeological applications such as the characterization of aquifer heterogeneity

parameter estimation by ensemble kalman filters with transformed data approach and application to hydraulic tomography 1 ensemble kalman filters enkfs are a successful tool for estimating state variables in atmospheric and oceanic sciences recent research has prepared the enkf for parameter estimation in groundwater applications enkfs are optimal in the sense of bayesian updating only if all involved variables are multivariate gaussian subsurface flow and transport state variables however generally do not show gaussian dependence on hydraulic log conductivity and among each other even if log conductivity is multi gaussian to improve enkfs in this context we apply nonlinear monotonic transformations to the observed states rendering them gaussian gaussian anamorphosis ga similar ideas have recently been presented by beal et al 2010 in the context of state estimation our work transfers and adapts this methodology to parameter estimation additionally we address the treatment of measurement errors in the transformation and provide several multivariate analysis tools to evaluate the expected usefulness of ga beforehand for illustration we present a first time application of an enkf to parameter estimation from 3 d hydraulic tomography in multi gaussian log conductivity fields results show that 1 ga achieves an implicit pseudolinearization of drawdown data as a function of log conductivity and 2 this makes both parameter identification and prediction of flow and transport more accurate combining enkfs with ga yields a computationally efficient tool for nonlinear inversion of data with improved accuracy this is an attractive benefit given that linearization free methods such as particle filters are computationally extremely demanding

delay enhanced coherent chaotic oscillations in networks with large disorders we study the effect of coupling delay in a regular network with a ring topology and in a more complex network with an all to all global topology in the presence of impurities disorder we find that the coupling delay is capable of inducing phase coherent chaotic oscillations in both types of networks thereby enhancing the spatiotemporal complexity even in the presence of 50 of symmetric disorders of both fixed and random types furthermore the coupling delay increases the robustness of the networks up to 70 of disorders thereby preventing the network from acquiring periodic oscillations to foster disorder induced spatiotemporal order we also confirm the enhancement of coherent chaotic oscillations using snapshots of the phases and values of the associated kuramoto order parameter we also explain a possible mechanism for the phenomenon of delay induced coherent chaotic oscillations despite the presence of large disorders and discuss its applications

building a suited reduced modal basis for updating 3d acoustic models with the constitutive law error method abstract we have recently reported the possibility of developing an updating technique for acoustic finite element models based on the constitutive law error proposed by p ladeveze and co workers in structural dynamics like with every updating technique we are confronted with and interested in reducing the computational time the main idea of this paper consists in building a reduced modal basis made of two contributions static modes complete a truncated modal basis corresponding to the frequency range of computation the static modes are associated to the system excitation for instance a normal velocity boundary condition but also to the system damping and to the reference measurements updating acoustic models using the reduced modal basis shows a significant cpu time saving with respect to the full non reduced system with an acceptable accuracy

an energy error based method for the resolution of the cauchy problem in 3d linear elasticity a new method is described for the problem of expanding known displacement fields at the boundary of a solid together with the surface tractions on it towards the solid interior up to the inaccessible part of the boundary the solid is supposedly linearly elastic with known elastic moduli but not necessarily homogeneous nor isotropic the problem is the cauchy problem for the lame operator a new form of this cauchy problem suited for applications associated with surface tangential fields measurements is also stated and studied the method is based on the splitting of the elastic fields into two separate solutions of wellposed problems the gap between these fields is subsequently minimised with respect to the unknown boundary data in order to produce the desired expanded elastic fields the gap used here is an energy error associated with the elastic energy of the system various 3d applications are given including non linear boundary conditions on the unreachable boundary

real time groundwater flow modeling with the ensemble kalman filter joint estimation of states and parameters and the filter inbreeding problem 1 real time groundwater flow modeling with filter methods is interesting for dynamical groundwater flow systems for which measurement data in real time are available the ensemble kalman filter enkf approach is used here to update states together with parameters by adopting an augmented state vector approach the performance of enkf is investigated in a synthetic study with a two dimensional transient groundwater flow model where 1 only the recharge rate is spatiotemporally variable 2 only transmissivity is spatially variable with lnt2 1 0 or 3 with lnt2 2 7 and 4 both recharge rate and transmissivity are uncertain a combination of 1 and 3 the performance of enkf for simultaneous state and parameter estimation in saturated groundwater flow problems is investigated in dependence of the number of stochastic realizations the updating frequency and updating intensity of log transmissivity the amount of measurements in space and time and the method iterative versus noniterative enkf among others satisfactory results were also obtained if both transmissivity and recharge rate were uncertain however it was found that filter inbreeding is much more severe if hydraulic heads and transmissivities are jointly updated than if only hydraulic heads are updated the filter inbreeding problem was investigated in more detail and could be strongly reduced with help of a damping parameter which limits the intensity of the perturbation of the log transmissivity field an additional reduction of filter inbreeding could be achieved by combining two measures 1 inflating the elements of the predicted state covariance matrix on the basis of a comparison between the model uncertainty and the observed errors at the measurement points and 2 starting the flow simulations with a very large number of realizations and then sampling the desired number of realizations after one simulation time step by minimizing the differences between the local cpdfs and bivariate cpdfs of hydraulic head for the large ensemble and the corresponding cpdfs for the reduced ensemble the two measures which cause very limited cpu costs allowed making 100 stochastic realizations for the reproduction of the states as efficient as 200 500 untreated stochastic realizations

a variational approach for parameter estimation based on balanced proper orthogonal decomposition abstract the adjoint method has been used significantly for parameter estimation this requires a significant programming effort to build adjoint model code and is computationally expensive as cost of one adjoint simulation often exceeds several original model runs the work proposed here is variational data assimilation based on balanced proper orthogonal decomposition bpod to identify uncertain parameters in numerical models and avoids the implementation of the adjoint with respect to model input parameters an ensemble of model simulations forward and backward is used to determine the model subspace while considering both inputs and outputs of the system by projecting the original model onto this subspace an approximate linear reduced model is obtained the adjoint of the tangent linear model is replaced by the adjoint of linear reduced model and the minimization problem is then solved in the reduced space at very low computational cost the performance of the method are illustrated with a number of data assimilation experiments in a 2d advection diffusion model the results demonstrate that the bpod based estimation approach successfully estimates the diffusion coefficient for both advection and diffusion dominated problems the paper also proposes an efficient method for computing the observable subspace when the number of observations is large is also proposed

geostatistical inverse modeling of transient pumping tests using temporal moments of drawdown 1 pumping tests belong to the most common techniques of hydrogeological site assessment while the steady state drawdown is determined by the distribution of transmissivity alone the transient behavior is also influenced by the storativity field in geostatistical inverse modeling the spatial distributions of both transmissivity and storativity are inferred from the drawdown curves and prior information on the spatial correlation of the parameter fields so far however transient data have hardly been analyzed by geostatistical inverse methods because the computational effort is rather high in the present study we characterize the drawdown by its temporal moments we present moment generating equations and corresponding equations to compute the sensitivity of the temporal moments of drawdown with respect to the distributions of transmissivity and storativity we utilize these equations to infer the transmissivity and storativity fields from transient pumping tests using the quasi linear geostatistical approach of inverse modeling considering temporal moments rather than full drawdown curves drastically reduces the computational effort of the estimation procedure in test cases we show that the first two temporal moments are sufficient to characterize the drawdown curves we investigate how erroneous assumptions regarding the spatial variability of storativity affect the estimate of the transmissivity field and we analyze the effect of truncating the measured drawdown curves

a tutorial on particle filters for online nonlinear non gaussian bayesian tracking increasingly for many application areas it is becoming important to include elements of nonlinearity and non gaussianity in order to model accurately the underlying dynamics of a physical system moreover it is typically crucial to process data on line as it arrives both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics in this paper we review both optimal and suboptimal bayesian algorithms for nonlinear non gaussian tracking problems with a focus on particle filters particle filters are sequential monte carlo methods based on point mass or particle representations of probability densities which can be applied to any state space model and which generalize the traditional kalman filtering methods several variants of the particle filter such as sir asir and rpf are introduced within a generic framework of the sequential importance sampling sis algorithm these are discussed and compared with the standard ekf through an illustrative example

dimensionality reduction and polynomial chaos acceleration of bayesian inference in inverse problems we consider a bayesian approach to nonlinear inverse problems in which the unknown quantity is a spatial or temporal field endowed with a hierarchical gaussian process prior computational challenges in this construction arise from the need for repeated evaluations of the forward model e g in the context of markov chain monte carlo and are compounded by high dimensionality of the posterior we address these challenges by introducing truncated karhunen loeve expansions based on the prior distribution to efficiently parameterize the unknown field and to specify a stochastic forward problem whose solution captures that of the deterministic forward model over the support of the prior we seek a solution of this problem using galerkin projection on a polynomial chaos basis and use the solution to construct a reduced dimensionality surrogate posterior density that is inexpensive to evaluate we demonstrate the formulation on a transient diffusion equation with prescribed source terms inferring the spatially varying diffusivity of the medium from limited and noisy data

a thermodynamic counterpart of the axelrod model of social influence the one dimensional case abstract we propose a thermodynamic version of the axelrod model of social influence in one dimensional 1d lattices the thermodynamic model becomes a coupled potts model with a bonding interaction that increases with the site matching traits we analytically calculate thermodynamic and critical properties for a 1d system and show that an order disorder phase transition only occurs at t 0 independent of the number of cultural traits q and features f the 1d thermodynamic axelrod model belongs to the same universality class of the ising and potts models notwithstanding the increase of the internal dimension of the local degree of freedom and the state dependent bonding interaction we suggest a unifying proposal to compare exponents across different discrete 1d models the comparison with our hamiltonian description reveals that in the thermodynamic limit the original out of equilibrium 1d axelrod model with noise behaves like an ordinary thermodynamic 1d interacting particle system

on the role of the propensity score in efficient semiparametric estimation of average treatment effects in this paper the role of the propensity score in the efficient estimation of average treatment effects is examined under the assumption that the treatment is ignorable given some observed characteristics it is shown that the propensity score is ancillary for estimation of the average treatment effects the propensity score is not ancillary for estimation of average treatment effects on the treated it is suggested that the marginal value of the propensity score lies entirely in the dimension reduction efficient semiparametric estimators of average treatment effects and average treatment effects on the treated are shown to take the form of relevant sample averages of the data completed by the nonparametric imputation method it is shown that the projection on the propensity score is not necessary for efficient semiparametric estimation of average treatment effects on the treated even if the propensity score is known an application to the experimental data reveals that conditioning on the propensity score may even result in a loss of efficiency

multistable binary decision making on networks we propose a simple model for a binary decision making process on a graph motivated by modeling social decision making with cooperative individuals the model is similar to a random field ising model or fiber bundle model but with key differences on heterogeneous networks for many types of disorder and interactions between the nodes we predict discontinuous phase transitions with mean field theory which are largely independent of network structure we show how these phase transitions can also be understood by studying microscopic avalanches and describe how network structure enhances fluctuations in the distribution of avalanches we suggest theoretically the existence of a glassy spectrum of equilibria associated with a typical phase even on infinite graphs so long as the first moment of the degree distribution is finite this behavior implies that the model is robust against noise below a certain scale and also that phase transitions can switch from discontinuous to continuous on networks with too few edges numerical simulations suggest that our theory is accurate

a perspective on adaptive modeling and meshing am m the purpose of this study is to develop an integrated approach to adaptive modeling in conjunction with adaptive meshing this involves consideration of the modeling error from various sources and the discretization error in the numerical scheme adapting the model to control error due to geometry boundary data constitutive relations and model parameters can be integrated with adaptive mesh control to improve numerical reliability other factors such as stabilization by artificial dissipation and regularization are considered a class of hybrid algorithms are developed to combine these approaches

improvement of a large analytical model using test data a method has been developed which uses measured normal modes and natural frequencies to improve an analytical mass and stiffness matrix model of a structure the method directly identifies without iteration a set of minimum changes in the analytical matrices which force the eigensolutions to agree with the test measurements an application is presented in which the analytical model had 508 degrees of freedom and 19 modes were measured at 101 locations on the structure the resulting changes in the model are judged to be small compared to expectations of error in the analysis thus the improved model is accepted as a reasonable model of the structure with improved dynamic response characteristics in addition it is shown that the procedure may be a useful tool in identifying apparent measured modes which are not true normal modes of the structure nomenclature analytical matrix matrix of changes identity matrix full improved stiffness and mass matrices n x n full analytical k m matrices n x n partitions of ka ma corresponding to test coordinates partitions of ka ma corresponding to coupling elements partitions of ka ma corresponding to unmea sured coordinates number of measured modes number of degrees of freedom in the model measures of changes eqs 15 17 matrix norm sum of the squares of all the elements rectangular modal matrix normalized n x m th mode th column of measured and unmeasured partitions of diagonal matrix of measured natural frequencies m xm natural frequency of th mode 12 17 sum of the squares of all elements of matrix

improved double robust estimation in missing data and causal inference models recently proposed double robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double robust estimators under misspecification of the outcome model in this paper we derive a new class of double robust estimators for the parameters of regression models with incomplete cross sectional or longitudinal data and of marginal structural mean models for cross sectional data with similar efficiency properties unlike the recent proposals our estimators solve outcome regression estimating equations in a simulation study the new estimator shows improvements in variance relative to the standard double robust estimator that are in agreement with those suggested by asymptotic theory

high dimensional variable selection this paper explores the following question what kind of statistical guarantees can be given when doing variable selection in high dimensional models in particular we look at the error rates and power of some multi stage regression methods in the first stage we fit a set of candidate models in the second stage we select one model by cross validation in the third stage we use hypothesis testing to eliminate some variables we refer to the first two stages as screening and the last stage as cleaning we consider three screening methods the lasso marginal regression and forward stepwise regression our method gives consistent variable selection under certain conditions

effects of noise and confidence thresholds in nominal and metric axelrod dynamics of social influence we study the effects of bounded confidence thresholds and of interaction and external noise on axelrod s model of social influence our study is based on a combination of numerical simulations and an integration of the mean field master equation describing the system in the thermodynamic limit we find that interaction thresholds affect the system only quantitatively but that they do not alter the basic phase structure the known crossover between an ordered and a disordered state in finite systems subject to external noise persists in models with general confidence threshold interaction noise here facilitates the dynamics and reduces relaxation times we also study axelrod systems with metric features and point out similarities and differences compared to models with nominal features

doubly robust estimation in missing data and causal inference models the goal of this article is to construct doubly robust dr estimators in ignorable missing data and causal inference models in a missing data model an estimator is dr if it remains consistent when either but not necessarily both a model for the missingness mechanism or a model for the distribution of the complete data is correctly specified because with observational data one can never be sure that either a missingness model or a complete data model is correct perhaps the best that can be hoped for is to find a dr estimator dr estimators in contrast to standard likelihood based or nonaugmented inverse probability weighted estimators give the analyst two chances instead of only one to make a valid inference in a causal inference model an estimator is dr if it remains consistent when either a model for the treatment assignment mechanism or a model for the distribution of the counterfactual data is correctly specified because with observational data one can never be sure that a model for the treatment assignment mechanism or a model for the counterfactual data is correct inference based on dr estimators should improve upon previous approaches indeed we present the results of simulation studies which demonstrate that the finite sample performance of dr estimators is as impressive as theory would predict the proposed method is applied to a cardiovascular clinical trial

modified constitutive relation error identification strategy for transient dynamics with corrupted data the elastic case abstract in this paper we present an identification strategy for transient dynamic tests with corrupted data and its first development for the identification of elastic properties this method is based on the modified constitutive relation error principle which was initially introduced for model updating in vibration analysis the governing principle of the method consists in splitting the equations of the problem into two groups a reliable group and an uncertain group and defining mechanical fields which verify the equations of the reliable group exactly and those of the uncertain group only approximately then these fields are used to estimate the quality of the model to be identified we tested our method on various examples and found it to be very robust with respect to high levels of perturbations furthermore we show that this good performance is due to the fact that the method takes into account all the experimental data in a single calculation and introduces a distance between the measurements and the calculation we also propose an alternative strategy dedicated to cases where there is no a priori knowledge of the perturbations

acceleration of uncertainty updating in the description of transport processes in heterogeneous materials the prediction of thermo mechanical behaviour of heterogeneous materials such as heat and moisture transport is strongly influenced by the uncertainty in parameters such materials occur e g in historic buildings and the durability assessment of these therefore needs a reliable and probabilistic simulation of transport processes which is related to the suitable identification of material parameters in order to include expert knowledge as well as experimental results one can employ an updating procedure such as bayesian inference the classical probabilistic setting of the identification process in bayes form requires the solution of a stochastic forward problem via computationally expensive sampling techniques which makes the method almost impractical in this paper novel stochastic computational techniques such as the stochastic galerkin method are applied in order to accelerate the updating procedure the idea is to replace the computationally expensive forward simulation via the conventional finite element fe method by the evaluation of a polynomial chaos expansion pce such an approximation of the fe model for the forward simulation perfectly suits the bayesian updating the presented uncertainty updating techniques are applied to the numerical model of coupled heat and moisture transport in heterogeneous materials with spatially varying coefficients defined by random fields

slicing regression a link free regression method slicing regression a link free regression m e t h o d author s naihua duan and k e r c h a u l i s o u r c e the annals of statistics v o l 19 n o 2 t u n 1991 p p 5 0 5 5 3 0 p u b l i s h e d b y institute of mathematical statistics s t a b l e u r l http www jstor org stable 2242072 accessed 16 05 2011 your use of the jstor archive indicates your acceptance of jstor s terms and conditions of use available at http www jstor org page info about policies terms jsp jstor s terms and conditions of use provides in part that unless you have obtained prior permission you may not download an entire issue of a journal or multiple copies of articles and you may use content in the jstor archive only for your personal non commercial use please contact the publisher regarding any further use of this work publisher contact information may be obtained at http www jstor org action showpublisher publishercode ims each copy of any part of a jstor transmission must contain the same copyright notice that appears on the screen or printed page of such transmission jstor is a not for profit service that helps scholars researchers and students discover use and build upon a wide range of content in a trusted digital archive we use information technology and tools to increase productivity and facilitate new forms of scholarship for more information about jstor please contact support jstor org http www jstor org

a distributional approach for causal inference using propensity scores drawing inferences about the effects of treatments and actions is a common challenge in economics epidemiology and other fields we adopt rubin s potential outcomes framework for causal inference and propose two methods serving complementary purposes one can be used to estimate average causal effects assuming no confounding given measured covariates the other can be used to assess how the estimates might change under various departures from no confounding both methods are developed from a nonparametric likelihood perspective the propensity score plays a central role and is estimated through a parametric model under the assumption of no confounding the joint distribution of covariates and each potential outcome is estimated as a weighted empirical distribution expectations from the joint distribution are estimated as weighted averages or equivalently to first order regression estimates the likelihood estimator is at least as efficient and the regression estimator is at least as efficient and r

local versus global interactions in nonequilibrium transitions a model of social dynamics a nonequilibrium system of locally interacting elements in a lattice with an absorbing order disorder phase transition is studied under the effect of additional interacting fields these fields are shown to produce interesting effects in the collective behavior of this system both for autonomous and external fields disorder grows in the system when the probability of the elements to interact with the field is increased there exists a threshold value of this probability beyond which the system is always disordered the domain of parameters of the ordered regime is larger for nonuniform local fields than for spatially uniform fields however the zero field limit is discontinous in the limit of vanishingly small probability of interaction with the field autonomous or external fields are able to order a system that would fall in a disordered phase under local interactions of the elements alone we consider different types of fields which are interpreted as forms of mass media acting on a social system in the context of axelrod s model for cultural dissemination

a semiparametric approach to dimension reduction we provide a novel and completely different approach to dimension reduction problems from the existing literature we cast the dimension reduction problem in a semiparametric estimation framework and derive estimating equations viewing this problem from the new angle allows us to derive a rich class of estimators and obtain the classical dimension reduction techniques as special cases in this class the semiparametric approach also reveals that in the inverse regression context while keeping the estimation structure intact the common assumption of linearity and or constant variance on the covariates can be removed at the cost of performing additional nonparametric regression the semiparametric estimators without these common assumptions are illustrated through simulation studies and a real data example this article has online supplementary material

the existence of negative absolute temperatures in axelrod s social influence model abstract we introduce the concept of temperature as an order parameter in the standard axelrod s social influence model it is defined as the relation between suitably defined entropy and energy functions t s e 1 we show that at the critical point where the order disorder transition occurs this absolute temperature changes in sign at this point which corresponds to the transition homogeneous heterogeneous culture the entropy of the system shows a maximum we discuss the relationship between the temperature and other properties of the model in terms of cultural traits

highly parameterized inverse estimation of hydraulic conductivity and porosity in a three dimensional heterogeneous transport experiment 1 assessing the impact of parameter estimation accuracy in models of heterogeneous three dimensional 3 d groundwater systems is critical for predictions of solute transport a unique experimental data set provides concentration breakthrough curves btcs measured at a 0 253 cm3 scale over the 13 8 8 cm3 domain 53 000 measurement locations advective transport is used to match the first temporal moments of btcs or mean arrival times m1 averaged at 0 253 and 1 0 cm3 scales through simultaneous inversion of highly parameterized heterogeneous hydraulic conductivity k and porosity fields pilot points parameterize the fields within eight layers of the 3 d medium and estimations are completed with six different models of the k relationship parameter estimation through advective transport shows accurate estimation of the observed m1 values results across the six different k relationships have statistically similar fits to the observed m1 values and similar spatial estimates of m1 along the main flow direction the resulting fields provide the basis for forward transport modeling of the advection dispersion equation ade using the estimated k and fields demonstrates that advective transport coupled with inversion using dense spatial field parameterization provides an efficient surrogate for the ade these results indicate that there is not a single set of model parameters or a single k relationship that leads to a best representation of the actual experimental sand packing pattern i e nonuniqueness additionally knowledge of the individual sand k and values along with their arrangement in the 3 d experiment does not reproduce the observed transport results at small scales small scale variation in the packing and mixing of the sands causes large deviations from the expected transport results as highlighted in forward ade simulations highly parameterized inverse estimation is able to identify those regions where variations in mixing and packing alter the expected property values and significantly improve results relative to the naive application of the experimentally derived property values impacts of the observation scale the scale over which results are averaged and the number of observations and parameters on the final estimations are also examined results indicate existence of a representative element volume rev at 0 25 cm3 the existence of subgrid scale heterogeneity that impacts transport and the accuracy of highly parameterized models with even relatively small amounts of observations finally this work suggests that local heterogeneity features below the rev scale are difficult to incorporate into parameterized models highlighting the importance of addressing prediction uncertainty for small scale variability i e uncaptured variability in modeling practice