pattern recognition by graph matching using the potts mft neural networks this paper is concerned with programming of the potts mean field theory neural networks for pattern recognition by homomorphic mapping of the attributed relational graphs arg in order to generate the homomorphic mapping from the scene relational graph to the model graph we make use of the recently introduced suganthan technical report nanyang technical university 1994 compatibility functions in relation to the hopfield network an efficient pose clustering algorithm is used to separate and localize different occurrences of any particular object model in the scene the pose clustering algorithm also eliminates spurious hypotheses generated by the network and resolves ambiguities in the final interpretation the performance of the proposed approach to pattern recognition by homomorphism is demonstrated using a number of line patterns silhouette images and circle patterns

connection between the burgers equation with an elastic forcing term and a stochastic process in this paper a complete analytical resolution of the one dimensional burgers equation with the elastic forcing term k2x f t k is an element of r is presented two methods existing for the case k 0 are adapted and generalized using variable and functional transformations valid for all values of space and time the emergence of a fokker planck equation in the method allows the establishment of a connection between the burgers equation and the ornstein uhlenbeck process

remarks on competitive coexistence the asymptotic behavior of two predators competing exploitatively for the same prey in a constant environment is analyzed in this paper assuming that the prey have fast dynamics it is proved through a singular perturbation argument that the two predators can coexist if the prey and predators parameters lie within specified ranges it is also shown that coexistence occurs through a simple periodic behavior a poor season characterized by an almost endemic presence of the prey alternates with a rich season during which prey are abundant and predators regenerate this property follows from the geometric nature of the equilibrium manifold and is therefore generic the results are in agreement with some conjectures supported by computer simulation and with the most recent theoretical findings on competitive coexistence

asymptotic properties of weighted m estimators for variable probability samples i provide a systematic treatment of the asymptotic properties of weighted m estimators under variable probability stratified sampling the characterization of the sampling scheme and representation of the objective function allow for a straightforward analysis simple consistent asymptotic variance matrix estimators are proposed for a large class of problems when stratification is based on exogenous variables i show that the unweighted m estimator is more efficient than the weighted estimator under a generalized conditional information matrix equality when population frequencies are known a more efficient weighting is possible i also show how the results carry over to multinomial sampling

pattern formation in the brusselator system in the paper we deal with a reaction diffusion system well known as the brusselator model and some improved results for the steady states of this model are presented we first give an a priori estimates positive upper and lower bounds of positive steady states then we obtain the non existence and existence of positive non constant steady states as the parameters and b are varied which means some certain conditions under which the pattern formation occurs or not

a graduated assignment algorithm for graph matching a graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise by combining graduated nonconvexity two way assignment constraints and sparsity large improvements in accuracy and speed are achieved its low order computational complexity o lm where l and m are the number of links in the two graphs and robustness in the presence of noise offer advantages over traditional combinatorial approaches the algorithm not restricted to any special class of graph is applied to subgraph isomorphism weighted graph matching and attributed relational graph matching to illustrate the performance of the algorithm attributed relational graphs derived from objects are matched then results from twenty five thousand experiments conducted on 100 mode random graphs of varying types graphs with only zero one links weighted graphs and graphs with node attributes and multiple link types are reported no comparable results have been reported by any other graph matching algorithm before in the research literature twenty five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated

matching on the estimated propensity score propensity score matching estimators rosenbaum and rubin 1983 are widely used in evaluation research to estimate average treatment effects in this article we derive the large sample distribution of propensity score matching estimators our derivations take into account that the propensity score is itself estimated in a first step prior to matching we prove that first step estimation of the propensity score affects the large sample distribution of propensity score matching estimators and derive adjustments to the large sample variances of propensity score matching estimators of the average treatment effect ate and the average treatment effect on the treated atet the adjustment for the ate estimator is negative or zero in some special cases implying that matching on the estimated propensity score is more efficient than matching on the true propensity score in large samples however for the atet estimator the sign of the adjustment term depends on the data generating process and ignoring the estimation error in the propensity score may lead to confidence intervals that are either too large or too small

efficient semiparametric estimation of multi valued treatment effects under ignorability abstract this paper studies the efficient estimation of a large class of multi valued treatment effects as implicitly defined by a collection of possibly over identified non smooth moment conditions when the treatment assignment is assumed to be ignorable two estimators are introduced together with a set of sufficient conditions that ensure their n consistency asymptotic normality and efficiency under mild assumptions these conditions are satisfied for the marginal mean treatment effect and the marginal quantile treatment effect estimands of particular importance for empirical applications previous results for average and quantile treatments effects are encompassed by the methods proposed here when the treatment is dichotomous the results are illustrated by an empirical application studying the effect of maternal smoking intensity during pregnancy on birth weight and a monte carlo experiment

mesa type patterns in the one dimensional brusselator and their stability the brusselator is a generic reaction diffusion model for a tri molecular chemical reaction we consider the case when the input and output reactions are slow in this limit we show the existence of k periodic spatially bi stable structures mesas and study their stability using singular perturbation techniques we find a threshold for the stability of k mesas this threshold occurs in the regime where the exponentially small tails of the localized structures start to interact by comparing our results with turing analysis we show that in the generic case a turing instability is followed by a slow coarsening process whereby logarithmically many mesas are annihilated before the system reaches a steady equilibrium state we also study a breather type instability of a mesa which occurs due to a hopf bifurcation full numerical simulations are shown to confirm the analytical results in 1952 turing proposed that the formation of spatial patterns during morphogenesis could be explained in terms of the instability of a homogeneous steady state solution of a reaction diffusion network describing the evolution of a set of morphogens 31 turing himself illustrated his ideas on two chemical models turing s original work is primarily concerned with the stability analysis of the homogeneous steady state solution of the rate equations for the interacting morphogens 22 the main point of biological interest however is whether stable spatial structures may be generated beyond the instability i e whether the rate equations admit stable and positive inhomogeneous solutions exhibiting the most characteristic features of morphogenetic patterns this point has been taken up seriously in the early seventies more systematic numerical studies of turing s model have been performed showing irregular spatial structures 4 this led to serious reservations about the relevance of turing s theory in developmental biology particularly its ability to generate regular patterns but these criticisms originate from a somewhat unfortunate choice of turing s example and they do not touch the essential points of turing s theory more specifically the obvious requirement that the rate equations must admit positive and bounded solutions is not satisfied in turing s example 11

hopf bifurcation and stability in predator prey model with a stage structure for prey a predator prey system with b d functional response and stage structure for prey is presented in the d t plane the regions of stability and hopf bifurcation are found by analyzing the associated characteristic transcendental equation further global stability of equilibria are studied by using comparison theorem some numerical simulations are also given to illustrate our results

weighted nonparametric maximum likelihood estimate of a mixing distribution in nonrandomized clinical trials hierarchical models have a variety of applications including multi center clinical trials local estimation of disease rates longitudinal studies risk assessment and meta analysis in a hierarchical model observations are sampled conditional on individual unit specific parameters and these parameters are sampled from a mixing distribution in observational studies or nonrandomized clinical trails observations may be biased samples from a population and heterogeneous with respect to some confounding factors without controlling the heterogeneity in the sample the standard estimation of the mixing distribution may lead to inaccurate statistical inferences in this article we propose a weighted nonparametric maximum likelihood estimate npmle of the mixing distribution and its smoothed version via weighted smoothing by roughening the proposed estimator reduces bias by assigning a weight to each subject in the sample the weighted npmle is shown to be weighted self consistent and therefore can be easily calculated through a recursive approach simulation studies were conducted to evaluate the performance of the proposed estimator we applied this method to clinical trial data evaluating a new treatment for stress urinary incontinence

global stability for a class of predator prey systems this paper deals with the question of global stability of the positive locally asymptotically stable equilibrium in a class of predator prey systems the dulac s criterion is applied and liapunov functions are constructed to establish the global stability

exact solutions for a forced burgers equation with a linear external force abstract we investigate the solutions of the burgers equation t u x t d x 2 u x t x f x t u x t u x t x u x t x t where f x t is an external force and x t represents a forcing term this equation is first analyzed in the absence of the forcing term by taking f x t k 1 t k 2 t x into account for this case the solution obtained extends the usual one present in the ornstein uhlenbeck process and depending on the choice of k 1 t and k 2 t it can present a stationary state or an anomalous spreading afterwards the forcing terms x t 1 t 2 t x and x t 3 x 4 x 3 are incorporated in the previous analysis and exact solutions are obtained for both cases

delay distribution dependent exponential stability criteria for discrete time recurrent neural networks with stochastic delay this brief is concerned with the analysis problem of global exponential stability in the mean square sense for a class of linear discrete time recurrent neural networks drnns with stochastic delay different from the prior research works the effects of both variation range and probability distribution of the time delay are involved in the proposed method first a modeling method is proposed by translating the probability distribution of the time delay into parameter matrices of the transformed drnn model where the delay is characterized by a stochastic binary distributed variable based on the new method the global exponential stability in the mean square sense for the drnns with stochastic delay is investigated by using the lyapunov krasovskii functional and exploiting some new analysis techniques a numerical example is provided to show the effectiveness and the applicability of the proposed method

structural matching by discrete relaxation this paper describes a bayesian framework for performing relational graph matching by discrete relaxation our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching broadly speaking there are two main aspects to this study firstly we focus on the issue of how relational inexactness may be quantified we illustrate that several popular relational distance measures can be recovered as specific limiting cases of the bayesian consistency measure the second aspect of our comparison concerns the way in which structural inexactness is controlled we investigate three different realizations of the matching process which draw on contrasting control models the main conclusion of our study is that the active process of graph editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter

diagnostic testing and evaluation of maximum likelihood models abstract the paper develops a unified theory of likelihood specification testing based on m estimators of auxiliary parameters the theory is sufficiently general to encompass a wide class of specification tests including moment based tests pearson type goodness of fit tests the information matrix test and the cox test the paper also presents a framework based on frechet differentiation for determining the effects of misspecification on the almost sure limits of parameter estimates and specification test statistics

gmm redundancy results for general missing data problems we consider questions of efficiency and redundancy in the gmm estimation problem in which we have two sets of moment conditions where two sets of parameters enter into one set of moment conditions while only one set of parameters enters into the other we then apply these results to a selectivity problem in which the first set of moment conditions is for the model of interest and the second set of moment conditions is for the selection process we use these results to explain the counterintuitive result in the literature that under an ignorability assumption that justifies gmm with weighted moment conditions weighting using estimated probabilities of selection is better than weighting using the true probabilities we also consider estimation under an exogeneity of selection assumption such that both the unweighted and the weighted moment conditions are valid and we show that when weighting is not needed for consistency it is also not useful for efficiency

hopfield network with constraint parameter adaptation for overlapped shape recognition we propose an energy formulation for homomorphic graph matching by the hopfield network and a lyapunov indirect method based learning approach to adaptively learn the constraint parameter in the energy function the adaptation scheme eliminates the need to specify the constraint parameter empirically and generates valid and better quality mappings than the analog hopfield network with a fixed constraint parameter the proposed hopfield network with constraint parameter adaptation is applied to match silhouette images of keys and results are presented

valid inference for treatment effect parameters under irregular identification and many extreme propensity scores abstract this paper provides a framework for conducting valid inference for causal parameters without imposing strong variance or support restrictions on the propensity score in particular it covers the case of irregularly identified treatment effect parameters we provide limit theorems for inverse probability weighting and doubly robust estimation of causal or counterfactual parameters that do not rely on trimming approaches by construction the limiting distributions of these estimators belong to the alpha stable class which implies that standard inference methods such as the nonparametric bootstrap are inconsistent we propose an adaptive version of the m out of n bootstrap that is robust to all types of identification and a bootstrap aggregation method for the optimal m choice monte carlo simulations suggest that the modified resampling method compares favorably to conventional methods in finite samples the method is applied to a re analysis of the causal impact of right heart catheterization on survival rates

conditional moment models with data missing at random summarywe consider a general statistical model defined by moment restrictions when data are missing at random using inverse probability weighting we show that such a model is equivalent to a model for the observed variables only augmented by a moment condition defined by the missingness mechanism our framework covers parametric and semiparametric mean regressions and quantile regressions we allow for missing responses missing covariates and any combination of them the equivalence result sheds new light on various aspects of missing data and provides guidelines for building efficient estimators

specification testing and quasi maximum likelihood estimation abstract this paper develops robust regression based conditional moment tests for models estimated by quasi maximum likelihood using a density in the linear exponential family the tests are relatively simple to compute while being robust to distributional assumptions other than those being explicitly tested the conditional mean tests assume only that the conditional mean is correctly specified under the null hypothesis but are asymptotically equivalent to standard forms of the tests if the conditional variance is correctly specified tests for conditional variance misspecification assume only that the first two moments are correctly specified under the null hypothesis an empirical application to individual arrest data is included

on the dynamics of predator prey models with the beddington deangelis functional response abstract the dynamics of predator prey models with the beddington deangelis functional response are analyzed primarily from the viewpoint of permanence uniform persistence the beddington deangelis functional response is similar to the holling type 2 functional response but contains an extra term describing mutual interference by predators both spatially homogeneous models based on ordinary differential equations and reaction diffusion models are considered criteria for permanence and for predator extinction are derived for systems without diffusion or with no flux boundary conditions criteria are derived for the existence of a globally stable coexistence equilibrium or alternatively for the existence of periodic orbits

the performance of estimators based on the propensity score we investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observed covariates is required like inverse probability weighting kernel and other variants of matching as well as different parametric models the simulation design used is based on real data usually employed for the evaluation of labour market programmes in germany we vary several dimensions of the design that are of practical importance like sample size the type of the outcome variable and aspects of the selection process we find that trimming individual observations with too much weight as well as the choice of tuning parameters are important for all estimators a conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall in particular when robustness to misspecifications of the propensity score and different types of outcome variables is considered an important property

a surface based approach to 3 d object recognition using a mean field annealing neural network abstract three dimensional 3 d object recognition identifies objects in an input image using a modelbase we present a 3 d object recognition system in which a symbolic description of the object is generated from the input range data in terms of the visible surface patches the segmented surface representation of an object is regarded as a graph in which nodes contain the information about the individual surface patches whereas links represent the relationships between them such as connectivity object recognition is achieved by matching the object graph of a scene with the model graph of a modelbase in the proposed approach a mean field annealing mfa neural network nn is employed as a constraint satisfaction network in which an energy function robust to occlusion is proposed for effective matching the energy level is at its minimum when the optimal match is achieved simulation results with synthetic and real range images having various occlusion rates are presented to show the effectiveness of the proposed algorithm

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

robust inference using inverse probability weighting inverse probability weighting ipw is widely used in empirical work in economics and other disciplines as gaussian approximations perform poorly in the presence of small denominators trimming

a random cellular automaton related to the noisy burgers equation abstract we derive a random cellular automaton ca model from the noisy burgers equation the method for that derivation is a nonanalytic limiting procedure called ultra discretization method udm proposed by tokihiro et al phys rev lett 76 1996 3247 special attention is paid to the noise term it is the intention of this paper to propose a random ca model which could be used as a starting point to understand on a ca basis the precise mechanism leading from deterministic evolutionary equations which trigger chaos such as the kuramoto shivashinsky ks equation to equivalent stochastic large scale descriptions we investigate our ca model to ensure that it belongs to the same universality class as the noisy burgers and thereby also ks equation itself

on a periodic multi species ecological model a periodic predator prey model with m predators and n preys is proposed in this paper which can be seen as the modification of the traditional lotka volterra model by using comparison theorem the ultimately bounded region of the system is obtained by using comparison theorem and brouwer fixed point theorem sufficient conditions with guarantee the existence of a positive periodic solution of the system is obtained finally by constructing a suitable lyapunov function some sufficient conditions are obtained for the existence of a unique globally attractive periodic solution of system the results obtained here generalized the main results of j d zhao w c chen global asymptotic stability of a periodic ecological model applied mathematics and computation 147 3 2004 881 892

improving semiparametric estimation by using surrogate data summary the paper considers estimating a parameter that defines an estimating function u y x for an outcome variable y and its covariate x when the outcome is missing in some of the observations we assume that in addition to the outcome and the covariate a surrogate outcome is available in every observation the efficiency of existing estimators for depends critically on correctly specifying the conditional expectation of u given the surrogate and the covariate when the conditional expectation is not correctly specified which is the most likely scenario in practice the efficiency of estimation can be severely compromised even if the propensity function of missingness is correctly specified we propose an estimator that is robust against the choice of the conditional expectation via an empirical likelihood we demonstrate that the estimator proposed achieves a gain in efficiency whether the conditional score is correctly specified or not when the conditional score is correctly specified the estimator reaches the semiparametric variance bound within the class of estimating functions that are generated by u the practical performance of the estimator is evaluated by using simulation and a data set that is based on the 1996 us presidential election

quantile regression under random censoring censored regression models have received a great deal of attention in both the theoretical and applied econometric literature most of the existing estimation procedures for either cross sectional or panel data models are designed only for models with fixed censoring in this paper a new procedure for adapting these estimators designed for fixed censoring to models with random censoring is proposed this procedure is then applied to the clad and quantile estimators of powell j econom 25 1984 303 32 1986a 143 to obtain an estimator of the coefficients under a mild conditional quantile restriction on the error term that is applicable to samples exhibiting fixed or random censoring the resulting estimator is shown to have desirable asymptotic properties and performs well in a small scale simulation study

dynamics of the density dependent predator prey system with beddington deangelis functional response abstract two models of a density dependent predator prey system with beddington deangelis functional response are systematically considered one includes the time delay in the functional response and the other does not the explorations involve the permanence local asymptotic stability and global asymptotic stability of the positive equilibrium for the models by using stability theory of differential equations and lyapunov functions for the permanence the density dependence for predators is shown to give some negative effect for the two models further the permanence implies the local asymptotic stability for a positive equilibrium point of the model without delay also the global asymptotic stability condition which can be easily checked for the model is obtained for the model with time delay local and global asymptotic stability conditions are obtained

hopfield neural networks for affine invariant matching the affine transformation which consists of rotation translation scaling and shearing transformations can be considered as an approximation to the perspective transformation therefore it is very important to find an effective means for establishing point correspondences under affine transformation in many applications in this paper we consider the point correspondence problem as a subgraph matching problem and develop an energy formulation for affine invariant matching by a hopfield type neural network the fourth order network is investigated first then order reduction is done by incorporating the neighborhood information in the data thus we can use the second order hopfield network to perform subgraph isomorphism invariant to affine transformation which can be applied to an affine invariant shape recognition problem experimental results show the effectiveness and efficiency of the proposed method

regression analysis with randomly right censored data this paper proposes a new estimator of the parameter vector in a linear regression model when the observations are randomly censored on the right and when the error distribution is unknown this estimator is explicitly defined and easily computable the paper contains sufficient conditions under which this estimator is mean square consistent and asymptotically normal a numerical example is given

ignorability and coarse data we present a general statistical model for data coarsening which includes as special cases rounded heaped censored partially categorized and missing data formally with coarse data observations are made not in the sample space of the random variable of interest but rather in its power set grouping is a special case in which the degree of coarsening is known and nonstochastic we establish simple conditions under which the possible stochastic nature of the coarsening mechanism can be ignored when drawing bayesian and likelihood inferences and thus the data can be validly treated as grouped data the conditions are that the data be coarsened at random a generalization of the condition missing at random and that the parameters of the data and the coarsening process be distinct applications of the general model and the ignorability condition are illustrated in a numerical example and described briefly in a variety of special cases

partial survival and extinction of a delayed predator prey model with stage structure abstract in this paper we revisit the non persistent property of a stage structured predator prey system which was proposed by ma et al 2008 z h ma z z li s f wang t li f p zhang permanence of a predator prey system with stage structure and time delay appl math comput 201 2008 pp 65 71 by applying the standard comparison theorem some novel results concern with the extinction of the system and partial survival of the predator prey species respectively are obtained one of the interesting finding is that for this system the extinction of prey may not necessarily lead to extinction of predator

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

a new instrumental method for dealing with endogenous selection this paper develops a new method for dealing with endogenous selection the usual instrumental strategy based on the independence between the outcome and the instrument is likely to fail when selection is directly driven by the dependent variable instead we suggest to rely on the independence between the instrument and the selection variable conditional on the outcome this approach may be particularly suitable for nonignorable nonresponse binary models with missing covariates or roy models with an unobserved sector the nonparametric identification of the joint distribution of the variables is obtained under a completeness assumption which has been used recently in several nonparametric instrumental problems even if the conditional independence between the instrument and the selection variable fails to hold the approach provides sharp bounds on parameters of interest under weaker monotonicity conditions apart from identification nonparametric and parametric estimations are also considered finally the method is applied to estimate the effect of grade retention in french primary schools

semiparametric efficiency in multivariate regression models with missing data abstract we consider the efficiency bound for the estimation of the parameters of semiparametric models defined solely by restrictions on the means of a vector of correlated outcomes y when the data on y are missing at random we show that the semiparametric variance bound is the asymptotic variance of the optimal estimator in a class of inverse probability of censoring weighted estimators and that this bound is unchanged if the data are missing completely at random for this case we study the asymptotic performance of the generalized estimating equations gee estimators of mean parameters and show that the optimal gee estimator is inefficient except for special cases the optimal weighted estimator depends on unknown population quantities but for monotone missing data we propose an adaptive estimator whose asymptotic variance can achieve the bound

shape indexing using self organizing maps in this paper we propose a novel approach to generate the topology preserving mapping of structural shapes using self organizing maps soms the structural information of the geometrical shapes is captured by relational attribute vectors these vectors are quantised using an som using this som a histogram is generated for every shape these histograms are treated as inputs to train another som which yields a topology preserving mapping of the geometric shapes by appropriately choosing the relational vectors it is possible to generate a mapping that is invariant to some chosen transformations such as rotation translation scale affine or perspective transformations experimental results using trademark objects are presented to demonstrate the performance of the proposed methodology

estimating causal effects for multivalued treatments a comparison of approaches interventions with multivalued treatments are common in medical and health research such as when comparing the efficacy of competing drugs or interventions or comparing between various doses of a particular drug in recent years there has been a growing interest in the development of multivalued treatment effect estimators using observational data in this paper we compare the performance of commonly used regression based methods that estimate multivalued treatment effects based on the unconfoundedness assumption these estimation methods fall into three general categories i estimators based on a model for the outcome variable using conventional regression adjustment ii weighted estimators based on a model for the treatment assignment and iii doubly robust estimators that model both the treatment assignment and outcome variable within the same framework we assess the performance of these models using monte carlo simulation and demonstrate their application with empirical data our results show that i when models estimating both the treatment and outcome are correctly specified all adjustment methods provide similar unbiased estimates ii when the outcome model is misspecified regression adjustment performs poorly while all the weighting methods provide unbiased estimates iii when the treatment model is misspecified methods based solely on modeling the treatment perform poorly while regression adjustment and the doubly robust models provide unbiased estimates and iv when both the treatment and outcome models are misspecified all methods perform poorly given that researchers will rarely know which of the two models is misspecified our results support the use of doubly robust estimation

turbulence without pressure we develop exact field theoretic methods to treat turbulence when the effect of pressure is negligible we find explicit forms of certain probability distributions demonstrate that the breakdown of galilean invariance is responsible for intermittency and establish the operator product expansion we also indicate how the effects of pressure can be turned on perturbatively

semiparametric regression estimation in the presence of dependent censoring summary we propose a semiparametric estimation procedure for estimating the regression of an outcome y measured at the end of a fixed follow up period on baseline explanatory variables x measured prior to start of follow up in the presence of dependent censoring given x the proposed estimators are consistent when the data are missing at random but not missing completely at random rubin 1976 and do not require full specification of the complete data likelihood specifically we assume that the probability of censoring at time t is independent of the outcome y conditional on the recorded history up to t of a vector of time dependent covariates that are correlated with y our estimators can be used to adjust for dependent censoring and nonrandom noncompliance in randomised trials studying the effect of a treatment on the mean of a response variable of interest even with independent censoring our methods allow the investigator to increase efficiency by exploiting the correlation of the outcome with a vector of time dependent covariates

using a monotone single index model to stabilize the propensity score in missing data problems and causal inference the augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems an important component of this method is the propensity score popular parametric models for the propensity score include the logistic probit and complementary log log models a common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables to avoid the need to choose a model we model the propensity score via a semiparametric single index model in which the score is an unknown monotonic nondecreasing function of the given single index under this new model the augmented inverse weighting estimator aiwe of the mean of the response is asymptotically linear semiparametrically efficient and more robust than existing estimators moreover we have made a surprising observation the inverse probability weighting and aiwes based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model a heuristic explanation of this phenomenon is provided a real data example is used to illustrate the proposed methods

dynamics of a nonautonomous predator prey system with the beddington deangelis functional response abstract in this paper we systematically study the dynamics of a nonautonomous predator prey system with the beddington deangelis functional response the explorations involve the permanence extinction global asymptotic stability general nonautonomous case the existence uniqueness and stability of a positive almost periodic solution and a boundary almost periodic solution for the periodic almost periodic case the paper ends with some interesting numerical simulations that complement our analytical findings

a stage structured predator prey model of beddington deangelis type we formulate and study a robust stage structured predator prey model of beddington deangelis type functional response the time delay is the time taken from birth to maturity the beddington deangelis functional response is similar to the holling type 2 functional response but contains an extra term describing mutual interference by predators first we show that the predator coexists with prey permanently if and only if the predator s recruitment rate at the peak of prey abundance is larger than its death rate second we show that if the system is permanent then a sufficiently large degree of the predator interference can not only stabilize the system but also guarantee the stability of the system against the increase of the carrying capacity of prey and the increase of birth rate of the adult predator third we show both analytically and numerically that stability switches of interior equilibrium may occur as maturation time delay increases stability may change from stable to unstable to finally stable implying that a large delay can be stabilizing

misclassification in binary choice models abstract bias from misclassification of binary dependent variables can be pronounced we examine what can be learned from such contaminated data first we derive the asymptotic bias in parametric models allowing misclassification to be correlated with observables and unobservables simulations and validation data show that the bias formulas are accurate in finite samples and in most situations imply attenuation second we examine the bias in a prototypical application erroneously restricting the covariance of misclassification and covariates aggravates the bias for all estimators we examine estimators that relax this restriction perform well if a model of misclassification or validation data is available

inverse probability weighted estimation for general missing data problems abstract i study inverse probability weighted m estimation under a general missing data scheme examples include m estimation with missing data due to a censored survival time propensity score estimation of the average treatment effect in the linear exponential family and variable probability sampling with observed retention frequencies i extend an important result known to hold in special cases estimating the selection probabilities is generally more efficient than if the known selection probabilities could be used in estimation for the treatment effect case the setup allows a general characterization of a double robustness result due to scharfstein et al 1999 rejoinder journal of the american statistical association 94 1135 1146

stability and bifurcation in a delayed predator prey system with beddington deangelis functional response we consider a delayed predator prey system with beddington deangelis functional response the stability of the interior equilibrium will be studied by analyzing the associated characteristic transcendental equation by choosing the delay as a bifurcation parameter we show that hopf bifurcation can occur as the delay crosses some critical values the direction and stability of the hopf bifurcation are investigated by following the procedure of deriving normal form given by faria and magalhaes an example is given and numerical simulations are performed to illustrate the obtained results

efficiency bounds for missing data models with semiparametric restrictions this paper shows that the semiparametric efficiency bound for a parameter identified by an unconditional moment restriction with data missing at random mar coincides with that of a particular augmented moment condition problem the augmented system consists of the inverse probability weighted ipw original moment restriction and an additional conditional moment restriction which exhausts all other implications of the mar assumption the paper also investigates the value of additional semiparametric restrictions on the conditional expectation function cef of the original moment function given always observed covariates in the program evaluation context for example such restrictions are implied by semiparametric models for the potential outcome cefs given baseline covariates the efficiency bound associated with this model is shown to also coincide with that of a particular moment condition problem some implications of these results for estimation are briefly discussed

analysis of semiparametric regression models for repeated outcomes in the presence of missing data abstract we propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data the proposed estimators do not require full specification of the likelihood they can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not missing completely at random these estimators can be used to correct for dependent censoring and nonrandom noncompliance in randomized clinical trials studying the effect of a treatment on the evolution over time of the mean of a response variable the likelihood based parametric g computation algorithm estimator may also be used to attempt to correct for dependent censoring and nonrandom noncompliance but because of possible model misspecification the parametric g computation algorithm estimator in contrast with the proposed w

on a nonlinear nonautonomous predator prey model with diffusion and distributed delay in this paper a nonlinear nonautonomous predator prey model with diffusion and continuous distributed delay is studied where all the parameters are time dependent the system which is composed of two patches has two species the prey can diffuse between two patches but the predator is confined to one patch we first discuss the uniform persistence and global asymptotic stability of the model after that by constructing a suitable lyapunov functional some sufficient conditions for the existence of a unique almost periodic solution of the system are obtained an example shows the feasibility of our main results

object recognition and articulated object learning by accumulative hopfield matching in this paper a novel object recognition method based on attributed relational graph matching is proposed which is called accumulative hopfield matching we first divide the scene graph into many sub graphs and a modified hopfield network is then constructed to obtain the sub graph isomorphism between each sub scene graph and model graph the final result is deduced by accumulating the solutions of all small sub networks comparing to the traditional hopfield network the proposed system has the advantage of finding homomorphic mappings between two graphs furthermore the system can be applied for articulated object recognition and visual model learning which is considered as a difficult topic till now the proposed method has been evaluated with real images

stability of the boundary solution of a nonautonomous predator prey system with the beddington deangelis functional response abstract the dynamics of a nonautonomous predator prey system with the beddington deangelis functional response is studied from the perspective of extinction of the predator with the help of a fluctuation lemma we obtain a set of new sufficient conditions on the global asymptotic stability of the boundary solution which means the extinction of the predator the result not only improves but also complements some existing ones moreover the result indicates that the effect of the parameters is accumulative rather than pointwise

permanence extinction and periodic solution of predator prey system with beddington deangelis functional response in this paper we study permanence extinction and periodic solution of periodic predator prey system with beddington deangelis functional response we provide a sufficient and necessary condition to guarantee the predator and prey species to be permanent in addition sufficient condition is derived for the existence of positive periodic solution this paper improves some main results obtained by fan and kuang m fan y kuang dynamics of nonautonomous predator prey system with the beddington deangelis functional response j math anal appl 295 2004 15 39

fixed effects random effects and gee what are the differences for analyses of longitudinal repeated measures data statistical methods include the random effects model fixed effects model and the method of generalized estimating equations we examine the assumptions that underlie these approaches to assessing covariate effects on the mean of a continuous dichotomous or count outcome access to statistical software to implement these models has led to widespread application in numerous disciplines however careful consideration should be paid to their critical assumptions to ascertain which model might be appropriate in a given setting to illustrate similarities and differences that might exist in empirical results we use a study that assessed depressive symptoms in low income pregnant women using a structured instrument with up to five assessments that spanned the pre natal and post natal periods understanding the conceptual differences between the methods is important in their proper application even though empirically they might not differ substantively the choice of model in specific applications would depend on the relevant questions being addressed which in turn informs the type of design and data collection that would be relevant

positive periodic solutions of neutral lotka volterra system with feedback control with the help of a continuation theorem based on gaines and mawhin s coincidence degree easily verifiable criteria are established for the global existence of positive periodic solutions of neutral lotka volterra system with periodic delays and feedback control our results extend and improve existing results and have further applications in population dynamics

intermittency exponents and energy spectrum of the burgers and kpz equations with correlated noise we numerically calculate the energy spectrum intermittency exponents and probability density p u 0 of the one dimensional burgers and kpz equations with correlated noise we have used pseudo spectral method for our analysis when of the noise variance of the burgers equation variance k 2 exceeds 3 2 large shocks appear in the velocity prole leading to hju k j 2 i k 2 and structure function hju x r t u x t j q i r suggesting that the burgers equation is intermittent for this range of for 1660 the prole is dominated by noise and the spectrum hjh k j 2 i of the corresponding kpz equation is in close agreement with the medina et al renormalization group predictions in the intermediate range 0 1 2 we have also calculated the energy cascade rates for all and found a constant ux for all 1 2 c 2000 published

solitons in the noisy burgers equation we investigate numerically the coupled diffusion advective type field equations originating from the canonical phase space approach to the noisy burgers equation or the equivalent kardar parisi zhang equation in one spatial dimension the equations support stable right hand and left hand solitons and in the low viscosity limit a long lived soliton pair excitation we find that two identical pair excitations scatter transparently subject to a size dependent phase shift and that identical solitons scatter on a static soliton transparently without a phase shift the soliton pair excitation and the scattering configurations are interpreted in terms of growing step and nucleation events in the interface growth profile finally we show that growing steps perform an anomalous random walk with dynamic exponent z 3 2

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

sufficient conditions for the existence positive periodic solutions of a class of neutral delay models with feedback control with the help of a continuation theorem based on gaines and mawhin s coincidence degree easily verifiable criteria are established for the global existence of positive periodic solutions of a neutral model with periodic delays and feedback controldndt n t a t email protected t n t email protected i 1nb i t n email protected i t email protected i 1nc i t n email protected i t email protected t u t email protected i 1nd i t u email protected i t dudt e t u t f t n t email protected i 1ng i t n email protected i t our results extend and improve existing results and have further applications in population dynamics

periodicity and limit cycle perturbation analysis of a predator prey model with interspecific species interference predator additional food and dispersal we formulate a robust mathematical model for two predators having an overlapping dietary niche breadth we use the beddington deangelis functional and numerical responses which are relevant in addressing the principle of competitive exclusion as species interact the stabilizing effect of additional food in relation to the relative diffusivity d and wave number k has been investigated stability dissipativity permanance persistence and periodicity of the model have been studied using the routine and limit cycle perturbation methods besides revealing irregular periodic travelling wave behaviour due to predator interference numerical results also show oscillatory temporal dynamics resulting from additional food supplements combined with high predation rates

complete mathematical analysis of predator prey models with linear prey growth and beddington deangelis functional response the dynamics of a predator prey model with a beddington deangelis functional response and linear intrinsic growth rate of the prey population is fully analyzed conditions on local and global stability of the interior equilibrium are established the equilibria type are determined all possible global asymptotic behaviors of the system are considered including the determination of the extinction conditions and existence of periodic orbits it is shown that mutual interference between predators can alone stabilize predator prey interactions even when only a linear intrinsic growth rate of the prey population is considered in the mathematical model additional biological implications and a set of numerical simulations supporting the analysis are also presented

positive periodic solutions of neutral predator prey model with beddington deangelis functional response by using a continuation theorem based on coincidence degree theory some new sufficient conditions are obtained for the existence of positive periodic solutions for a neutral predator prey model with the beddington deangelis functional response