estimating time evolving partial coherence between signals via multivariate locally stationary wavelet processes we consider the problem of estimating time localized cross dependence in a collection of nonstationary signals to this end we develop the multivariate locally stationary wavelet framework which provides a time scale decomposition of the signals and thus naturally captures the time evolving scale specific cross dependence between components of the signals under the proposed model we rigorously define and estimate two forms of cross dependence measures wavelet coherence and wavelet partial coherence these dependence measures differ in a subtle but important way the former is a broad measure of dependence which may include indirect associations i e dependence between a pair of signals that is driven by another signal conversely wavelet partial coherence measures direct linear association between a pair of signals i e it removes the linear effect of other observed signals our time scale wavelet partial coherence estimation scheme thus provides a mechanism for identifying hidden dynamic relationships within a network of nonstationary signals as we demonstrate on electroencephalograms recorded in a visual motor experiment

symmetric prismatic tensegrity structures part ii symmetry adapted formulations this paper presents analytical formulations for the symmetry adapted equilibrium force density and geometrical stiffness matrices for prismatic tensegrity structures with dihedral symmetries an analytical expression for the infinitesimal mechanisms is also derived

multiscale methods for data on graphs and irregular multidimensional situations for regularly spaced one dimensional data wavelet shrinkage has proven to be a compelling method for non parametric function estimation we create three new multiscale methods that provide wavelet like transforms both for data arising on graphs and for irregularly spaced spatial data in more than one dimension the concept of scale still exists within these transforms but as a continuous quantity rather than dyadic levels further we adapt recent empirical bayesian shrinkage techniques to enable us to perform multiscale shrinkage for function estimation both on graphs and for irregular spatial data we demonstrate that our methods perform very well when compared with several other methods for spatial regression for both real and simulated data although we concentrate on multiscale shrinkage regression we present our new wavelet transforms as generic tools intended to be the basis of methods that might benefit from a multiscale representation of data either on graphs or for irregular spatial data

complex valued wavelet lifting and applications abstractsignals with irregular sampling structures arise naturally in many fields in applications such as spectral decomposition and nonparametric regression classical methods often assume a regular sampling pattern thus cannot be applied without prior data processing this work proposes new complex valued analysis techniques based on the wavelet lifting scheme that removes one coefficient at a time our proposed lifting transform can be applied directly to irregularly sampled data and is able to adapt to the signal s characteristics as our new lifting scheme produces complex valued wavelet coefficients it provides an alternative to the fourier transform for irregular designs allowing phase or directional information to be represented we discuss applications in bivariate time series analysis where the complex valued lifting construction allows for coherence and phase quantification we also demonstrate the potential of this flexible methodology over real valued analysis in the nonparametric re

informational content of special regressors in heteroskedastic binary response models we quantify the informational content of special regressors in heteroskedastic binary response models with median independent or conditionally symmetric errors based on lewbel 1998 a special regressor is additively separable in the latent payoff and conditionally independent from the error term we find that with median independent errors a special regressor does not increase the identifying power by a criterion in manski 1988 or lead to positive fisher information for the coefficients even though it does help recover the average structural function with conditionally symmetric errors a special regressor improves the identifying power and the information for coefficients is positive under mild conditions we propose two estimators for binary response models with conditionally symmetric errors and special regressors

articulated mechanism design with a degree of freedom constraint this paper deals with design of articulated mechanism using a truss based ground structure representation the proposed method can accommodate extremely large displacement by considering geometric non linearity in addition it can also control the mechanical degrees of freedom dof of the resultant mechanism by using a dof equation based on maxwell s rule the optimization is based on a relaxed formulation of an original integer problem and also involves developments directed at handling the redundancy inherent in the ground structure representation one planar test example is selected as the basis for the developments so as to compare the proposed method with other alternative approaches including a graph theoretical enumeration approach which guarantees the identification of the globally optimal solution also an inverter problem is treated where a continuation method is required in order to direct the optimization algorithm towards an integer solution copyright 2004 john wiley sons ltd

asymptotic efficiency in semi parametric models with censoring abstract we consider a regression model subject to bivariate censoring in which the errors are independent of the explanatory variables our objective is to drop the assumption of bivaraite normality and obtain an information bound on asymptotic efficiency when the error distribution is unrestricted except for smoothness and regularity conditions this bound has simple form if there are no exclusion restrictions on the selection equation then positive information requires a restriction on the regression slope parameters and a continuous distribution for a component of the explanatory variables we also consider a binary choice model under the weak assumption that the error distribution has zero median conditional on the explanatory variables here the semi parametric information bound is zero hence although a consistent estimator exists it is not possible to attain covergence at rate 1 n

matrix analysis of statically and kinematically indeterminate frameworks abstract the paper is concerned with the structural mechanics of assemblies of bars and pinjoints particularly where they are simultaneously statically and kinematically indeterminate the physical significance of the four linear algebraic vector subspaces of the equilibrium matrix is examined and an algorithm is set up which determines the rank of the matrix and the bases for the four subspaces in particular this algorithm gives full details of any states of self stress and modes of inextensional deformation which an assembly may possess a scheme is devised for the segregation of inextensional modes into rigid body modes up to six of these may be allowed by the foundation constraints and internal mechanisms in some circumstances a state of self stress may impart first order stiffness to an inextensional mode a matrix method for detecting this effect is devised and it is shown that if there is no state of self stress which imparts first order stiffness to a given mode then that mode can undergo rather large distortion which involves either zero change in length of the bars or possibly changes in length of third or higher order in the displacements the significance of negative stiffness as indicated by the matrix method is discussed the paper contains simple examples which illustrate all of the main points of the work

identification of complete information games this paper establishes sufficient conditions for point identification of the utility functions in generalized complete information game models these models allow generalized interaction structures and generalized behavioral assumptions the generalized interaction structures allow that the dependence of an agent s utility function on the other agents actions can itself depend on characteristics of the agents including an endogenous network of connections among the agents the generalized behavioral assumptions relax the solution concept from nash equilibrium play to weaker solution concepts like rationalizability the results allow a non parametric specification of the unobservables

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

symmetry adapted equilibrium matrices a number of authors have previously used group representation theory to block diagonalise the stiffness matrix of a symmetric structure this paper describes how similar techniques can be used to block diagonalise the equilibrium matrix of a symmetric structure this is shown to provide useful insight into the static and kinematic response of such systems in particular it simplifies finding and classifying states of self stress and mechanisms as well as reducing the computational effort required for a force method analysis

inference in semiparametric binary response models with interval data this paper studies the semiparametric binary response model with interval data investigated by manski and tamer 2002 in this partially identified model we propose a new estimator based on mt s modified maximum score mms method by introducing density weights to the objective function which allows us to develop asymptotic properties of the proposed set estimator for inference we show that the density weighted mms estimator converges at a nearly cube root n rate we propose an asymptotically valid inference procedure for the identified region based on subsampling monte carlo experiments provide supports to our inference procedure

path generation of articulated mechanisms by shape and topology variations in non linear truss representation this paper presents studies on an optimization based method for path generation of articulated mechanisms an extended truss ground structure approach is taken in which both the shape and topology of the truss are designed using cross sectional areas and nodal positions as design variables this leads to a technique for simultaneous type and dimensional synthesis of articulated mechanisms for the analysis part it is essential to control the mechanism configuration so that the mechanism remains within a given configuration space thus stabilizing the optimization process and resulting in realistic solutions this can be achieved by using the levenberg marquardt method the design method is illustrated by a number of design cases for both closed and open input and output paths

robust confidence intervals for average treatment effects under limited overlap limited overlap between the covariate distributions of groups with different treatment assignments does not only make estimates of average treatment effects rather imprecise but can also lead to substantially distorted confidence intervals this paper argues that this is because the coverage error of traditional confidence intervals is driven by the number of observations in the areas of limited overlap some of these local sample sizes can be very small in applications up to the point that distributional approximations derived from classical asymptotic theory become unreliable building on this observation this paper constructs confidence intervals based on classical approaches to small sample inference the approach is easy to implement and has superior theoretical and practical properties relative to standard methods in empirically relevant settings

real nonparametric regression using complex wavelets summary wavelet shrinkage is an effective nonparametric regression technique especially when the underlying curve has irregular features such as spikes or discontinuities the basic idea is simple take the discrete wavelet transform dwt of data consisting of a signal corrupted by noise shrink or remove the wavelet coefficients to remove the noise and then invert the dwt to form an estimate of the true underlying curve various authors have proposed increasingly sophisticated methods of doing this using real valued wavelets complex valued wavelets exist but are rarely used we propose two new shrinkage techniques which use complex valued wavelets extensive simulations show that our methods almost always give significantly more accurate estimates when compared with methods based on real valued wavelets further one of our methods is both simpler and dramatically faster than its competitors in an attempt to understand the excellent performance of this latter method we present a new risk bound on its hard thresholded coefficients

when is a symmetric pin jointed framework isostatic maxwell s rule from 1864 gives a necessary condition for a framework to be isostatic in 2d or in 3d given a framework with point group symmetry group representation theory is exploited to provide further necessary conditions this paper shows how for an isostatic framework these conditions imply very simply stated restrictions on the numbers of those structural components that are unshifted by the symmetry operations of the framework in particular it turns out that an isostatic framework in 2d can belong to one of only six point groups some conjectures and initial results are presented that would give sufficient conditions in both 2d and 3d for a framework that is realized generically for a given symmetry group to be an isostatic framework

semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables this paper provides estimators of discrete choice models including binary ordered and multinomial response choice models the estimators closely resemble ordinary and two stage least squares the distribution of the model s latent variable error is unknown and may be related to the regressors e g the model could have errors that are heteroscedastic or correlated with regressors the estimator does not require numerical searches even for multinomial choice for ordered and binary choice models the estimator is root n consistent and asymptotically normal a consistent estimator of the conditional error distribution is also provided

sieve m inference on irregular parameters abstract this paper presents sieve inferences on possibly irregular i e slower than root n estimable functionals of semi nonparametric models with i i d data we provide a simple consistent variance estimator of the plug in sieve m estimator of a possibly irregular functional and the asymptotic standard normality of the sieve t statistic we show that for hypothesis testing of irregular functionals the sieve likelihood ratio statistic is asymptotically chi square distributed these results are useful in inference on structural parameters that may have singular semiparametric efficiency bounds a simulation study and an empirical application of heckman and singer 1984 duration model are presented

a smoothed maximum score estimator for the binary response model this paper describes a semiparametric estimator for binary response models in which there may be arbitrary heteroskedasticity of unknown form the estimator is obtained by maximizing a smoothed version of the objective function of c manski s maximum score estimator the smoothing procedure is similar to that used in kernel nonparametric density estimation the resulting estimator s rate of convergence in probability is the fastest possible under the assumptions that are made the centered normalized estimator is asymptotically normally distributed methods are given for consistently estimating the parameters of the limiting distribution and for selecting the bandwidth required by the smoothing procedure copyright 1992 by the econometric society

motion estimation using a complex valued wavelet transform this paper describes a new motion estimation algorithm that is potentially useful for both computer vision and video compression applications it is hierarchical in structure using a separable two dimensional 2 d discrete wavelet transform dwt on each frame to efficiently construct a multiresolution pyramid of subimages the dwt is based on a complex valued pair of four tap fir filters with gabor like characteristics the resulting complex dwt cdwt effectively implements an analysis by an ensemble of gabor like filters with a variety of orientations and scales the phase difference between the subband coefficients of each frame at a given subpel bears a predictable relation to a local translation in the region of the reference frame subtended by that subpel that relation is used to estimate the displacement field at the coarsest scale of the multiresolution pyramid each estimate is accompanied by a directional confidence measure in the form of the parameters of a quadratic matching surface the initial estimate field is progressively refined by a coarse to fine strategy in which finer scale information is appropriately incorporated at each stage the accuracy efficiency and robustness of the new algorithm are demonstrated in comparison testing against hierarchical implementations of intensity gradient based and fractional precision block matching motion estimators

identifying the average treatment effect in ordered treatment models without unconfoundedness we show identification of the average treatment effect ate when treatment is specified by ordered choice in cross section or panel models treatment is determined by location of a latent variable containing a continuous instrument relative to two or more thresholds we place no functional form restrictions on latent errors and potential outcomes unconfoundedness of treatment does not hold and identification at infinity for the treated is not possible yet we still show nonparametric point identification and estimation of the ate we apply our model to reinvestigate the inverted u relationship between competition and innovation and find no inverted u in us data

the random coefficients logit model is identified the random coecients multinomial choice logit model also known as the mixed logit has been widely used in empirical choice analysis for the last thirty years we prove that the distribution of random coecients in the multinomial logit model is nonparametrically identified

a symmetry extension of maxwell s rule for rigidity of frames a symmetry extension of maxwell s rule for rigidity of frames is found this rule subsumes and strengthens maxwell s 1864 rule by requiring that the internal and external degrees of freedom of a pin jointed structure are not only numerically equal but are also equisymmetric a number of special cases of maxwell s original rule are studied to show the improved insight that the symmetry adapted version can give

model averaging in semiparametric estimation of treatment effects choosing the covariates and functional form of the propensity score is an important choice for estimating treatment effects this paper proposes a data driven way of averaging the estimators over candidate specifications to resolve the specification uncertainty in the propensity score weighting estimation of the att the proposed procedures minimize the estimated mse of the att estimator in a local asymptotic framework we formulate model averaging as a statistical decision problem in a limit experiment and derive an averaging scheme that is bayes optimal with respect to a given prior the averaging estimator outperforms selection estimators and the estimators in any of the candidate models in terms of bayes asymptotic mse our monte carlo studies illustrate the size of the mse gains we apply the averaging procedure to evaluate the effect of a labor market program

double link expandohedra a mechanical model for expansion of a virus double link expandohedra are introduced each is constructed from a parent polyhedron by replacing all faces with rigid plates adjacent plates being connected by a pair of spherically jointed bars powerful symmetry techniques are developed for mobility analysis of general double link expandohedra and when combined with numerical calculation and physical model building they demonstrate the existence of generic finite breathing expansion motions in many cases for icosahedrally symmetric trivalent parents with pentagonal and hexagonal faces only fullerene polyhedra the derived expandohedra provide a mechanical model for the experimentally observed swelling of viruses such as cowpea chlorotic mottle virus ccmv a fully symmetric swelling motion a finite mechanism is found for systems based on icosahedral fullerene polyhedra with odd triangulation number t 31 and is conjectured to exist for all odd triangulation numbers

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

wavelet processes and adaptive estimation of the evolutionary wavelet spectrum this article defines and studies a new class of non stationary random processes constructed from discrete non decimated wavelets which generalizes the cramer fourier representation of stationary time series we define an evolutionary wavelet spectrum ews which quantifies how process power varies locally over time and scale we show how the ews may be rigorously estimated by a smoothed wavelet periodogram and how both these quantities may be inverted to provide an estimable time localized autocovariance we illustrate our theory with a pedagogical example based on discrete non decimated haar wavelets and also a real medical time series example

estimation and inference for distribution functions and quantile functions in treatment effect models abstract we propose inverse probability weighted estimators for the distribution functions of the potential outcomes under the unconfoundedness assumption and apply the inverse mapping to obtain the quantile functions we show that these estimators converge weakly to zero mean gaussian processes a simulation method is proposed to approximate these limiting processes based on these results we construct tests for stochastic dominance relations between the potential outcomes monte carlo simulations are conducted to examine the finite sample properties of our tests we apply our test in an empirical example and find that a job training program had a positive effect on incomes

detection of finite mechanisms in symmetric structures abstract using group representation theory it is possible to block diagonalise the equilibrium matrix of a symmetric structure this analysis can identify the symmetry properties of any states of self stress or mechanisms present in the structure this paper will show that in some cases this linear analysis combined with symmetry arguments can show that particular mechanisms of a symmetric structure must be finite

identification and estimation in a correlated random coefficients binary response model we study a linear index binary response model with random coefficients bb allowed to be correlated with regressors x we identify the mean of the distribution of b and show how the mean can be interpreted as a vector of expected relative effects we use instruments and a control vector v to make x independent of b given v this leads to a localize then average approach to both identification and estimation we develop a n consistent and asymptotically normal estimator of a trimmed mean of the distribution of bb explore its small sample performance through simulations and present an application

binary response correlated random coefficient panel data models abstract in this paper we consider binary response correlated random coefficient crc panel data models which are frequently used in the analysis of treatment effects and demand of products we focus on the nonparametric identification and estimation of panel data models under unobserved heterogeneity which is captured by random coefficients and when these random coefficients are correlated with regressors our identification conditions and estimation are based on the framework of the model with a special regressor which is a novel approach proposed by lewbel 1998 lewbel 2000 to solve the heterogeneity and endogeneity problem in the binary response models with the help of the additional information on the special regressor we can transform a binary response crc model to a linear moment relation we also construct a semiparametric estimator for the average slopes and derive the n normality result further we propose a nonparametric method to test the correlations between random coefficients and regressors simulations are given to show the finite sample performance of our estimators and test statistics

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

semiparametric latent variable model estimation with endogenous or mismeasured regressors a simple root n consistent asymptotically normal semiparametric estimator of the coefficient vector b in the latent variable specification y l f b x e is constructed the distribution of e is unknown and may be correlated with x or be conditionally heteroscedastic e g x can contain measurement error the function l can also be unknown the identification assumption is that e is uncorrelated with instruments u and that the conditional distribution of e given x and u does not depend on one of the regressors which has some special properties extensions to more general latent variable specifications are provided

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

identification of binary response models abstract this article studies identification of the threshold crossing model of binary response most research on binary response has considered specific estimators and tests the study of identification exposes the foundations of binary response analysis by making explicit the assumptions needed to justify different methods it also clarifies the connections between reduced form and structural analyses of binary response data assume that the binary outcome z is determined by an observable random vector x and by an unobservable scalar u through a model z 1 x u 0 also assume that fu x the probability distribution of u conditional on x is continuous and strictly increasing given these maintained assumptions we investigate the identifiability of given the following restrictions on the distributions fu x x x mean independence quantile independence index sufficiency statistical independence and statistical independence with the distribution known we find that mean independence has n

a quantile correlated random coefficients panel data model abstract we propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data specifically we extend the correlated random coefficients representation of linear quantile regression e g koenker 2005 section 2 6 we show that panel data allows the econometrician to i introduce dependence between the regressors and the random coefficients and ii weaken the assumption of comonotonicity across them i e to enrich the structure of allowable dependence between different coefficients we adopt a fixed effects approach leaving any dependence between the regressors and the random coefficients unmodeled we motivate different notions of quantile partial effects in our model and study their identification for the case of discretely valued covariates we present analog estimators and characterize their large sample properties when the number of time periods t exceeds the number of random coefficients p identification is regular and our estimates are n consistent when t p our identification results make special use of the subpopulation of stayers units whose regressor values change little over time in a way which builds on the approach of graham and powell 2012 in this just identified case we study asymptotic sequences which allow the frequency of stayers in the population to shrink with the sample size one purpose of these discrete bandwidth asymptotics is to approximate settings where covariates are continuously valued and as such there is only an infinitesimal fraction of exact stayers while keeping the convenience of an analysis based on discrete covariates when the mass of stayers shrinks with n identification is irregular and our estimates converge at a slower than n rate but continue to have limiting normal distributions we apply our methods to study the effects of collective bargaining coverage on earnings using the national longitudinal survey of youth 1979 nlsy79 consistent with prior work e g chamberlain 1982 vella and verbeek 1998 we find that using panel data to control for unobserved worker heterogeneity results in sharply lower estimates of union wage premia we estimate a median union wage premium of about 9 percent but with in a more novel finding substantial heterogeneity across workers the 0 1 quantile of union effects is insignificantly different from zero whereas the 0 9 quantile effect is of over 30 percent our empirical analysis further suggests that on net unions have an equalizing effect on the distribution of wages

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

estimating linear dependence between nonstationary time series using the locally stationary wavelet model large volumes of neuroscience data comprise multiple nonstationary electrophysiological or neuroimaging time series recorded from different brain regions accurately estimating the dependence between such neural time series is critical since changes in the dependence structure are presumed to reflect functional interactions between neuronal populations we propose a new dependence measure derived from a bivariate locally stationary wavelet time series model since wavelets are localized in both time and scale this approach leads to a natural local and multi scale estimate of nonstationary dependence our methodology is illustrated by application to a simulated example and to electrophysiological data relating to interactions between the rat hippocampus and prefrontal cortex during working memory and decision making

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

irregular identification support conditions and inverse weight estimation in weighted moment condition models we show a subtle link between identification and estimability that limits the practical usefulness of estimators based on these models in particular if it is necessary for point identification that the weights take arbitrarily large values then the parameter of interest though point identified cannot be estimated at the regular parametric rate and is said to be irregularly identified this rate depends on relative tail conditions and can be as slow in some examples as n 1 4 this nonstandard rate of convergence can lead to numerical instability and or large standard errors we examine two weighted model examples i the binary response model under mean restriction introduced by lewbel 1997 and further generalized to cover endogeneity and selection where the estimator in this class of models is weighted by the density of a special regressor and ii the treatment effect model under exogenous selection rosenbaum and rubin 1983 where the resulting estimator of the average treatment effect is one that is weighted by a variant of the propensity score without strong relative support conditions these models similar to well known identified at infinity models lead to estimators that converge at slower than parametric rate since essentially to ensure point identification one requires some variables to take values on sets with arbitrarily small probabilities or thin sets for the two models above we derive some rates of convergence and propose that one conducts inference using rate adaptive procedures that are analogous to andrews and schafgans 1998 for the sample selection model

finite motions from periodic frameworks with added symmetry recent work from authors across disciplines has made substantial contributions to counting rules maxwell type theorems which predict when an infinite periodic structure would be rigid or flexible while preserving the periodic pattern as an engineering type framework or equivalently as an idealized molecular framework other work has shown that for finite frameworks introducing symmetry modifies the previous general counts and under some circumstances this symmetrized maxwell type count can predict added finite flexibility in the structure in this paper we combine these approaches to present new maxwell type counts for the columns and rows of a modified orbit matrix for structures that have both a periodic structure and additional symmetry within the periodic cells in a number of cases this count for the combined group of symmetry operations demonstrates there is added finite flexibility in what would have been rigid when realized without the symmetry given that many crystal structures have these added symmetries and that their flexibility may be key to their physical and chemical properties we present a summary of the results as a way to generate further developments of both a practical and theoretic interest

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

estimation of marginal effects in semiparametric selection models with binary outcomes this paper addresses the estimation of a semiparametric sample selection index model where both the selection rule and the outcome variable are binary since the marginal effects are often of primary interest and are difficult to recover in a semiparametric setting we focus on developing an estimator for the marginal effects this marginal effect estimator uses only observations where the selection probability is above a certain threshold a key innovation is that this high probability set is adaptive to the data we establish the large sample properties of the marginal effect estimator as well as those for an index estimator upon which it depends monte carlo studies show that these estimators perform well in finite samples

a parametric texture model based on joint statistics of complex wavelet coefficients we present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform the model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations orientations and scales we develop an efficient algorithm for synthesizing random images subject to these constraints by iteratively projecting onto the set of images satisfying each constraint and we use this to test the perceptual validity of the model in particular we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set we also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures

a symmetry treatment of danzerian rigidity for circle packing a symmetry extended mobility criterion that incorporates danzer s concept of bar and joint assemblies is derived and applied to the spherical circle packing problem the known scalar counting rule

semiparametric estimation of a simultaneous game with incomplete information abstract this paper studies the problem of estimating the normal form payoff parameters of a simultaneous discrete game where the realization of such payoffs is not common knowledge the paper contributes to the existing literature in two ways first by making a comparison with the complete information case it formally describes a set of conditions under which allowing for private information in payoffs facilitates the identification of various features of the game second focusing on the incomplete information case it presents an estimation procedure based on the equilibrium properties of the game that relies on weak semiparametric assumptions and relatively flexible information structures which allow players to condition their beliefs on signals whose exact distribution function is unknown to the researcher the proposed estimators recover unobserved beliefs by solving a semiparametric sample analog of the population bayesian nash equilibrium conditions the asymptotic features of such estimators are studied for the case in which the distribution of unobserved shocks is known and the case in which it is unknown in both instances equilibrium uniqueness is assumed to hold only in a neighborhood of the true parameter value and for a subset z of realizations of the signals multiple equilibria are allowed elsewhere in the parameter space and no equilibrium selection theory is involved extensions to games where beliefs are conditioned on unobservables as well as general games with many players and actions are also discussed an empirical application of a simple capital investment game is included

design of planar articulated mechanisms using branch and bound this paper considers an optimization model and a solution method for the design of two dimensional mechanical mechanisms the mechanism design problem is modeled as a nonconvex mixed integer program which allows the optimal topology and geometry of the mechanism to be determined simultaneously the underlying mechanical analysis model is based on a truss representation allowing for large displacements for mechanisms undergoing large displacements elastic stability is of major concern we derive conditions modeled by nonlinear matrix inequalities which guarantee that a stable equilibrium is found and that buckling is prevented the feasible set of the design problem is described by nonlinear differentiable and non differentiable constraints as well as nonlinear matrix inequalities to solve the mechanism design problem a branch and bound method based on convex relaxations is developed to guarantee convergence of the method two different types of convex relaxations are derived the relaxations are strengthened by adding valid inequalities to the feasible set and by solving bound contraction sub problems encouraging computational results indicate that the branch and bound method can reliably solve mechanism design problems of realistic size to global optimality

periodic frameworks and flexibility we formulate a concise deformation theory for periodic bar and joint frameworks in r d and illustrate our algebraic geometric approach on frameworks related to various crystalline structures particular attention is given to periodic frameworks modelled on silica zeolites and perovskites for frameworks akin to tectosilicates which are made of one skeleta of d dimensional simplices with each vertex common to exactly two simplices we prove the existence of a space of periodicity preserving infinitesimal flexes of dimension at least graphic 1 however these infinitesimal flexes need not come from genuine flexibility as shown by rigid examples the changes implicated in passing from a given lattice of periods to a sublattice of periods are illustrated with frameworks modelled on perovskites 1 embed inline graphic 1 gif

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

indirect inference with endogenously missing exogenous variables abstract we consider consistent estimation of parameters in a structural model by indirect inference ii when the exogenous variables can be missing at random mar endogenously we demonstrate that ii procedures that simply discard sample units with missing observations can yield inconsistent estimates of the true structural parameters by inverse probability weighting ipw the complete case observations i e sample units with no missing variables for the observed and simulated samples we propose a new method of ii to consistently estimate the structural parameters of interest asymptotic properties of the new estimator are discussed we consider a multinomial probit model to illustrate this approach and subsequently consider simulation studies in a variety of discrete choice models with and without dynamics in terms of lagged dependent variables and serially correlated errors the simulation results demonstrate the severe bias incurred by existing ii estimators and its correction by our new ii estimator

maxwell laman counts for bar joint frameworks in normed spaces abstract the rigidity matrix is a fundamental tool for studying the infinitesimal rigidity properties of euclidean bar joint frameworks in this paper we generalise this tool and introduce a rigidity matrix for bar joint frameworks in arbitrary finite dimensional real normed vector spaces using this new matrix we derive necessary maxwell laman type counting conditions for a well positioned bar joint framework in a real normed vector space to be infinitesimally rigid moreover we derive symmetry extended counting conditions for a bar joint framework with a non trivial symmetry group to be isostatic i e minimally infinitesimally rigid these conditions imply very simply stated restrictions on the number of those structural components that are fixed by the various symmetry operations of the framework finally we offer some observations and conjectures regarding combinatorial characterisations of symmetric isostatic bar joint frameworks in r 2 p where the unit ball p is a quadrilateral

geometry the leading parameter for the poisson s ratio of bending dominated cellular solids control over the deformation behaviour that a cellular structure shows in response to imposed external forces is a requirement for the effective design of mechanical metamaterials in particular those with negative poisson s ratio this article sheds light on the old question of the relationship between geometric microstructure and mechanical response by comparison of the deformation properties of bar and joint frameworks with those of their realisation as a cellular solid made from linear elastic material for ordered planar tessellation models we find a classification in terms of the number of degrees of freedom of the framework model first in cases where the geometry uniquely prescribes a single deformation mode of the framework model the mechanical deformation and poisson s ratio of the linearly elastic cellular solid closely follow those of the unique deformation mode the result is a bending dominated deformation with negligible dependence of the effective poisson s ratio on the underlying material s poisson s ratio and small values of the effective young s modulus second in the case of rigid structures or when geometric degeneracy prevents the bending dominated deformation mode the effective poisson s ratio is material dependent and the young s modulus view the mathml sourcee cs large all analysed structures of this type have positive values of the poisson s ratio and large values of view the mathml sourcee cs third in the case where the framework has multiple deformation modes geometry alone does not suffice to determine the mechanical deformation these results clarify the relationship between mechanical properties of a linear elastic cellular solid and its corresponding bar and joint framework abstraction they also raise the question if in essence auxetic behaviour is restricted to the geometry guided class of bending dominated structures corresponding to unique mechanisms with inherently low values of the young s modulus

sieve wald and qlr inferences on semi nonparametric conditional moment models this paper considers inference on functionals of semi nonparametric conditional moment restrictions with possibly nonsmooth generalized residuals which include all of the nonlinear nonparametric instrumental variables iv as special cases these models are often ill posed and hence it is difficult to verify whether a possibly nonlinear functional is root n estimable or not we provide computationally simple unified inference procedures that are asymptotically valid regardless of whether a functional is root n estimable or not we establish the following new useful results 1 the asymptotic normality of a plug in penalized sieve minimum distance psmd estimator of a possibly nonlinear functional 2 the consistency of simple sieve variance estimators for the plug in psmd estimator and hence the asymptotic chi square distribution of the sieve wald statistic 3 the asymptotic chi square distribution of an optimally weighted sieve quasi likelihood ratio qlr test under the null hypothesis 4 the asymptotic tight distribution of a non optimally weighted sieve qlr statistic under the null 5 the consistency of generalized residual bootstrap sieve wald and qlr tests 6 local power properties of sieve wald and qlr tests and of their bootstrap versions 7 asymptotic properties of sieve wald and sqlr for functionals of increasing dimension simulation studies and an empirical illustration of a nonparametric quantile iv regression are presented

on the choice of smoothing parameter threshold and truncation in nonparametric regression by non linear wavelet methods summary concise asymptotic theory is developed for non linear wavelet estimators of regression means in the context of general error distributions general designs general normalizations in the case of stochastic design and non structural assumptions about the mean the influence of the tail weight of the error distribution is addressed in the setting of choosing threshold and truncation parameters mainly the tail weight is described in an extremely simple way by a moment condition previous work on this topic has generally imposed the much more stringent assumption that the error distribution be normal different approaches to correction for stochastic design are suggested these include conventional kernel estimation of the design density in which case the interaction between the smoothing parameters of the non linear wavelet estimator and the linear kernel method is described

semiparametric analysis of discrete response asymptotic properties of the maximum score estimator abstract this paper is concerned with the estimation of the model med y x x from a random sample of observations on sgn y x manski 1975 introduced the maximum score estimator of the normalized parameter vector in the present paper strong consistency is proved it is also proved that the maximum score estimate lies outside any fixed neighborhood of with probability that goes to zero at exponential rate

semiparametric least squares sls and weighted sls estimation of single index models abstract for the class of single index models i construct a semiparametric estimator of coefficients up to a multiplicative constant that exhibits 1 n consistency and asymptotic normality this class of models includes censored and truncated tobit models binary choice models and duration models with unobserved individual heterogeneity and random censoring i also investigate a weighting scheme that achieves the semiparametric efficiency bound

the econometrics of ultra high frequency data ultra high frequency data is defined to be a full record of transactions and their associated characteristics the transaction arrival times and accompanying measures can be analyzed as marked point processes the acd point process developed by engle and russell 1998 is applied to ibm transactions arrival times to develop semiparametric hazard estimates and conditional intensities combining these intensities with a garch model of prices produces ultra high frequency measures of volatility both returns and variances are found to be negatively influenced by long durations as suggested by asymmetric information models of market micro structure

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

identification and estimation of games with incomplete information using excluded regressors the existing literature on binary games with incomplete information assumes that either payo functions or the distribution of private information are nitely parameterized to obtain point identi cation in contrast we show that given excluded regressors payofunctions and the distribution of private information can both be nonparametrically point identi ed an excluded regressor for player i is a su ciently varying state variable that does not aect other players utility and is additively separable from other components in i s payo we show how excluded regressors satis fying these conditions arise in contexts such as entry games between rms as variation in observed components of xed costs our identi cation proofs are constructive so consistent nonparametric estimators can be readily based on them for a semiparametric model with linear payos we propose root n consistent and asymptotically normal estimators for parameters in players payos finally we extend our approach to accommodate the existence of multiple bayesian nash equilibria in the data generating process without assuming equilibrium selection rules

extending the scope of wavelet regression methods by coefficient dependent thresholding abstract various aspects of the wavelet approach to nonparametric regression are considered with the overall aim of extending the scope of wavelet techniques to irregularly spaced data to regularly spaced datasets of arbitrary size to heteroscedastic and correlated data and to data that contain outliers the core of the methodology is an algorithm for finding ail of the variances and within level covariances in the wavelet table of a sequence with given covariance structure if the original covariance matrix is band limited then the algorithm is linear in the length of the sequence the variance calculation algorithm allows data on any set of independent variable values to be treated by first interpolating to a fine regular grid of suitable length and then constructing a wavelet expansion of the gridded data various thresholding methods are discussed and investigated exact risk formulas for the mean square error of the methodology for given design are derived good performance is obtained by nois

identification of panel data models with endogenous censoring this paper analyzes the identification question in censored panel data models where the censoring can depend on both observable and unobservable variables in arbitrary ways under some general conditions we derive the tightest sets on the parameter of interest these sets which can be singletons represent the limit of what one can learn about the parameter of interest given the model and the data in that every parameter that belongs to these sets is observationally equivalent to the true parameter we consider two separate sets of assumptions motivated by the previous literature each controlling for unobserved heterogeneity with an individual specific fixed effect the first imposes a stationarity assumption on the unobserved disturbance terms along the

generic combinatorial rigidity of periodic frameworks abstract we give a combinatorial characterization of generic minimal rigidity for planar periodic frameworks the characterization is a true analogue of the maxwell laman theorem from rigidity theory it is stated in terms of a finite combinatorial object and the conditions are checkable by polynomial time combinatorial algorithms to prove our rigidity theorem we introduce and develop periodic direction networks and z 2 graded sparse colored graphs

cube root asymptotics we establish a new functional central limit theorem for empirical processes indexed by classes of functions in a neighborhood of a fixed parameter point an n 1 3 rescaling of the parameter is compensated for by an n 2 3 rescaling of the empirical measure resulting in a limiting gaussian process by means of a modified continuous mapping theorem for the location of the maximizing value we deduce limit theorems for several statistics defined by maximization or constrained minimization of a process derived from the empirical measure these statistics include the short rousseeuw s least median of squares estimator manski s maximum score estimator and the maximum likelihood estimator for a monotone density the limit theory depends on a simple new sufficient condition for a gaussian process to achieve its maximum almost surely at a unique point