additive change detection in nonlinear systems with unknown change parameters we study the change detection problem in partially observed nonlinear systems which satisfy the hidden markov model hmm property the change parameters are assumed unknown and the changes can be slow or sudden a partially observed system needs to be tracked first before changes can be detected sudden changes result in significant loss of track these can be detected easily using the increase in tracking error te or observation likelihood ol or using a cusum type method applied to either of these however slow changes which result in small loss of track often get missed we propose here a statistic that uses the tracked component of the change to detect it and hence detects slow changes faster than te or ol we show both analytically and through simulations that this statistic complements ol and te for change detection

convergence of sph method for scalar nonlinear conservation laws this paper is devoted to the study of the convergence of weighted particle approximation of nonlinear multidimensional conservation laws for euler equations the method is closely related to the smooth particle hydrodynamics sph method extension of the original algorithm is proposed we use approximate riemann solvers instead of artificial viscosity in order to stabilize the scheme the mathematical analysis is performed by connecting this new approach with the finite volume scheme convergence of the approximate solution in l loc p p infty towards the unique weak entropy solution of the cauchy problem is obtained in the scalar nonlinear case by using uniqueness of measure valued solutions

efficient recursive algorithms for detection of abrupt changes in signals and control systems this paper addresses a number of open problems concerning the generalized likelihood ratio glr rules for online detection of faults and parameter changes in control systems it is shown that with an appropriate choice of the threshold and window size these glr rules are asymptotically optimal the rules are also extended to non likelihood statistics that are widely used in monitoring adaptive algorithms for system identification and control by establishing gaussian approximations to these statistics when the window size is chosen suitably recursive algorithms are developed for practical implementation of the procedure and importance sampling techniques are introduced for determining the threshold of the rule to satisfy prescribed bounds on the false alarm rate

sequential change point detection when unknown parameters are present in the pre change distribution in the sequential change point detection literature most research specifies a required frequency of false alarms at a given pre change distribution f and tries to minimize the detection delay for every possible post change distribution g in this paper motivated by a number of practical examples we first consider the reverse question by specifying a required detection delay at a given post change distribution and trying to minimize the frequency of false alarms for every possible pre change distribution f we present asymptotically optimal procedures for one parameter exponential families next we develop a general theory for change point problems when both the pre change distribution f and the post change distribution g involve unknown parameters we also apply our approach to the special case of detecting shifts in the mean of independent normal observations

semiparametric analysis of random effects linear models from binary panel data andersen 1970 considered the problem of inference on random effects linear models from binary response panel data he showed that inference is possible if the disturbances for each panel member are known to be white noise with the logistic distribution and if the observed explanatory variables vary over time a conditional maximum likelihood estimator consistently estimates the model parameters up to scale the present paper shows that inference remains possible if the disturbances for each panel member are known only to be time stationary with unbounded support and if the explanatory variables vary enough over time a conditional version of the maximum score estimator manski 1975 1985 consistently estimates the model parameters up to scale

renyi divergence and kullback leibler divergence renyi divergence is related to renyi entropy much like kullback leibler divergence is related to shannon s entropy and comes up in many settings it was introduced by renyi as a measure of information that satisfies almost the same axioms as kullback leibler divergence and depends on a parameter that is called its order in particular the renyi divergence of order 1 equals the kullback leibler divergence we review and extend the most important properties of renyi divergence and kullback leibler divergence including convexity continuity limits of sigma algebras and the relation of the special order 0 to the gaussian dichotomy and contiguity we also show how to generalize the pythagorean inequality to orders different from 1 and we extend the known equivalence between channel capacity and minimax redundancy to continuous channel inputs for all orders and present several other minimax results

robust hypothesis testing with a relative entropy tolerance this paper considers the design of a minimax test for two hypotheses where the actual probability densities of the observations are located in neighborhoods obtained by placing a bound on the relative entropy between actual and nominal densities the minimax problem admits a saddle point which is characterized the robust test applies a nonlinear transformation which flattens the nominal likelihood ratio in the vicinity of one results are illustrated by considering the transmission of binary data in the presence of additive noise

an alternative root n consistent estimator for panel data binary choice models in this paper we present an alternative root n consistent estimator for panel data fixed effects binary choice models the proposed estimator relaxes one of the key conditions that are required for the consistency of the estimator proposed in honore and lewbel 2002 and is shown to be consistent and asymptotically normally distributed under some sufficient conditions an easy to compute consistent estimator for the asymptotic covariance is provided

coupling of smoothed particle hydrodynamics with finite volume method for free surface flows a new algorithm for the solution of free surface flows with large front deformation and fragmentation is presented the algorithm is obtained by coupling a classical finite volume fv approach that discretizes the navier stokes equations on a block structured eulerian grid with an approach based on the smoothed particle hydrodynamics sph method implemented in a lagrangian framework the coupling procedure is formulated in such a way that each solver is applied in the region where its intrinsic characteristics can be exploited in the most efficient and accurate way the fv solver is used to resolve the bulk flow and the wall regions whereas the sph solver is implemented in the free surface region to capture details of the front evolution the reported results clearly prove that the combined use of the two solvers is convenient from the point of view of both accuracy and computing time a new algorithm for coupling meshless lagrangian methods and eulerian grid based methods is proposed a detailed validation using smoothed particle hydrodynamics sph and finite volume fv solvers is provided to our knowledge this is the first time that a coupling like the present one is proposed implemented and validated the intrinsic characteristics of both solvers can be exploited to efficiently and accurately solve complex free surface flows the results prove that the coupling strategy is convenient from the point of view of both accuracy and computing time

theoretical analysis and numerical verification of the consistency of viscous smoothed particle hydrodynamics formulations in simulating free surface flows the theoretical formulation of the smoothed particle hydrodynamics sph method deserves great care because of some inconsistencies occurring when considering free surface inviscid flows actually in sph formulations one usually assumes that i surface integral terms on the boundary of the interpolation kernel support are neglected ii free surface conditions are implicitly verified these assumptions are studied in detail in the present work for free surface newtonian viscous flow the consistency of classical viscous weakly compressible sph formulations is investigated in particular the principle of virtual work is used to study the verification of the free surface boundary conditions in a weak sense the latter can be related to the global energy dissipation induced by the viscous term formulations and their consistency numerical verification of this theoretical analysis is provided on three free surface test cases including a standing wave with the three viscous term formulations investigated

root n consistent semiparametric estimators of a dynamic panel sample selection model abstract this paper considers the problem of identification and estimation in panel data sample selection models with a binary selection rule when the latent equations contain possibly predetermined variables lags of the dependent variables and unobserved individual effects the selection equation contains lags of the dependent variables from both the latent and the selection equations as well as other possibly predetermined variables relative to the latent equations we derive a set of conditional moment restrictions that are then exploited to construct a three step sieve estimator for the parameters of the main equation including a nonparametric estimator of the sample selection term in the second step the unknown parameters of the selection equation are consistently estimated using a transformation approach in the spirit of berkson s minimum chi square sieve method and a first step kernel estimator for the selection probability this second step estimator is of interest in its own right it can be used to semiparametrically estimate a panel data binary response model with a nonparametric individual specific effect without making any other distributional assumptions we show that both estimators second and third stage are n consistent and asymptotically normal

estimation of additive regression models with known links summary we consider estimation of an additive nonparametric regression model with known link function the asymptotic distribution of this regression estimate is given the practical performance is investigated via an application to the study of migration between east and west germany

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

binary response models for panel data identification and information this paper considers a panel data model for predicting a binary outcome the conditional probability of a positive response is obtained by evaluating a given distribution function f at a linear combination of the predictor variables one of the predictor variables is unobserved it is a random effect that varies across individuals but is constant over time the semiparametric aspect is that the conditional distribution of the random effect given the predictor variables is unrestricted this paper has two results if the support of the observed predictor variables is bounded then identification is possible only in the logistic case even if the support is unbounded so that from manski 1987 identification holds quite generally the information bound is zero unless f is logistic hence consistent estimation at the standard pn rate is possible only in the logistic case

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

estimation of a nonlinear panel data model with semiparametric individual effects abstract this paper investigates identification and estimation of a class of nonlinear panel data single index models the model allows for unknown time specific link functions and semiparametric specification of the individual specific effects we develop an estimator for the parameters of interest and propose a powerful new kernel based modified backfitting algorithm to compute the estimator we derive uniform rates of convergence results for the estimators of the link functions and show the estimators of the finite dimensional parameters are root n consistent with a gaussian limiting distribution we study the small sample properties of the estimator via monte carlo techniques

minimax robust decentralized detection decentralized detection problems are studied where the sensor distributions are not specified completely the sensor distributions are assumed to belong to known uncertainty classes it is shown for a broad class of such problems that a set of least favorable distributions exists for minimax robust testing between the hypotheses it is hence established that the corresponding minimax robust tests are solutions to simple decentralized detection problems for which the sensor distributions are specified to be the least favorable distributions

robust hypothesis testing with alpha divergence a robust minimax test for two composite hypotheses which are determined by the neighborhoods of two nominal distributions with respect to a set of distances called alpha divergence distances is proposed sion s minimax theorem is adopted to characterize the saddle value condition least favorable distributions the robust decision rule and the robust likelihood ratio test are derived if the nominal probability distributions satisfy a symmetry condition the design procedure is shown to be simplified considerably the parameters controlling the degree of robustness are bounded from above and the bounds are shown to be resulting from a solution of a set of equations the simulations performed evaluate and exemplify the theoretical derivations

information bounds and quick detection of parameter changes in stochastic systems by using information theoretic bounds and sequential hypothesis testing theory this paper provides a new approach to optimal detection of abrupt changes in stochastic systems this approach not only generalizes previous work in the literature on optimal detection far beyond the relatively simple models treated but also suggests alternative performance criteria which are more tractable and more appropriate for general stochastic systems in addition it leads to detection rules which have manageable computational complexity for on line implementation and yet are nearly optimal under the different performance criteria considered

minimax robust quickest change detection the popular criteria of optimality for quickest change detection procedures are the lorden criterion the pollak criterion and the bayesian criterion in this paper a robust version of these quickest change detection problems is considered when the pre change and post change distributions are not known exactly but belong to known uncertainty classes of distributions for uncertainty classes that satisfy a specific condition it is shown that one can identify least favorable distributions lfds from the uncertainty classes such that the detection rule designed for the lfds is optimal for the robust problem in a minimax sense the condition is similar to that required for the identification of lfds for the robust hypothesis testing problem originally studied by huber an upper bound on the delay incurred by the robust test is also obtained in the asymptotic setting under the lorden criterion of optimality this bound quantifies the delay penalty incurred to guarantee robustness when the lfds can be identified the proposed test is easier to implement than the cusum test based on the generalized likelihood ratio glr statistic which is a popular approach for such robust change detection problems the proposed test is also shown to give better performance than the glr test in simulations for some parameter values

asymptotic minimax robust quickest change detection for dependent stochastic processes with parametric uncertainty in this paper we consider the problem of quickly detecting an unknown change in the conditional densities of a dependent stochastic process in contrast to the existing quickest change detection approaches for dependent stochastic processes we propose minimax robust versions of the popular lorden pollak and bayesian criteria for when there is uncertainty about the parameter of the post change conditional densities under an information theoretic pythagorean inequality condition on the uncertainty set of possible post change parameters we identify asymptotic minimax robust solutions to our lorden pollak and bayesian problems finally through simulation examples we illustrate that asymptotically minimax robust rules can provide detection performance comparable to the popular but more computationally expensive generalized likelihood ratio rule

variational and momentum preservation aspects of smooth particle hydrodynamic formulations this paper presents a new variational framework for various existing smooth particle hydrodynamic sph techniques and presents a new corrected sph formulation the linear and angular momentum preserving properties of sph formulations are also discussed the paper will show that in general in order to preserve angular momentum the sph equations must correctly evaluate the gradient of a linear velocity field a corrected algorithm that combines kernel correction with gradient correction is presented the paper will illustrate the theory presented with several examples relating to simple free surface flows

optimal stopping times for detecting changes in distributions it is shown that page s stopping time is optimal for the detection of changes in distributions in a well defined sense this work is a generalization of an existing result where it was shown that page s stopping time is optimal asymptotically

theoretical considerations on the free surface role in the smoothed particle hydrodynamics model in the present work an in depth analysis of the theoretical structure of the smoothed particle hydrodynamics hereinafter sph is provided for an inviscid weakly compressible and barotropic flow in the presence of a free surface the role of the free surface in the sph scheme is indeed little addressed in literature in the present analysis the general continuous formulation of the sph method is considered a detailed description of the free surface influence on the smoothed differential operators is supplied new and existing forms are analyzed in detail in terms of convergence and conservation properties the proposed analysis is based on the principle of virtual works which permits to exhibit the link with the enforcement of the dynamic free surface boundary condition finally possible sph formulations resulting from this analysis are investigated in terms of consistency conservation and dynamic free surface boundary condition

panel data discrete choice models with lagged dependent variables in this paper we consider identification and estimation in panel data discrete choice models when the explanatory variable set includes strictly exogenous variables lags of the endogenous dependent variable as well as unobservable individual specific effects for the binary logit model with the dependent variable lagged only once chamberlain 1993 gave conditions under which the model is not identified we present a stronger set of conditions under which the parameters of the model are identified the identification result suggests estimators of the model and we show that these are consistent and asymptotically normal although their rate of convergence is slower than the inverse of the square root of the sample size we also consider identification in the semiparametric case where the logit assumption is relaxed we propose an estimator in the spirit of the conditional maximum score estimator manski 1987 and we show that it is consistent in addition we discuss an extension of the identification result to multinomial discrete choice models and to the case where the dependent variable is lagged twice finally we present some monte carlo evidence on the small sample performance of the proposed estimators for the binary response model

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

another look at the instrumental variable estimation of error components models this article develops a framework for efficient iv estimators of random effects models with information in levels which can accommodate predetermined variables our formulation clarifies the relationship between the existing estimators and the role of transformations in panel data models we characterize the valid transformations for relevant models and show that optimal estimators are invariant to the transformation used to remove individual effects we present an alternative transformation for models with predetermined instruments which preserves the orthogonality among the errors finally we consider models with predetermined variables that have constant correlation with the effects and illustrate their importance with simulations

nonparametric estimation of a generalized additive model with an unknown link function mean of a random scalar y conditional on a random vector x e rd let h x e yix x denote the conditional mean function this paper is concerned with estimating h under weak assumptions about its functional form when x is a multidimensional continuous random variable the paper describes a model that nests single index additive and multiplicative models and that can be estimated with a faster rate of convergence in probability than can a fully nonparametric model nonparametric estimation of h is usually unattractive when x is multidimensional the rate of convergence of nonparametric estimators decreases rapidly as the dimension of x increases and nonparametric estimates can be very imprecise with samples of practical size when x is multidimensional the curse of dimensionality this problem can be overcome through the use of dimension reduction methods they reduce the effective dimension of the estimation problem by making assumptions about the functional form of h that are stronger than those of a fully nonparametric estimator but weaker than those of a finite dimensional parametric model one method of dimension reduction is semiparametric single index modeling the best known and most easily estimated semiparametric single index model assumes that 1 1 h x g 3 x where 3 is an unknown d x 1 vector and g is an unknown function the vector 3 can be estimated with an n 1 2 rate of convergence in probability which is the rate that would be obtained if g were known han 1987 hardle and stoker 1989 horowitz and hardle 1996 ichimura 1993 klein and spady 1993 newey and stoker 1993 powell stock and stoker 1989 sherman 1993 stoker 1991 g can be estimated with the rate of convergence n r 2r 1 where r is an integer that depends on the smoothness of g but not on d dimension reduction can also be achieved by using a nonparametric generalized additive model in such a model

binary choice panel data models with predetermined variables we present a class of binary choice models for panel data with the following features i the explanatory variables are predetermined but not strictly exogenous this includes lagged dependent variables as well as other forms of unspecified feedback ii individual effects are allowed to be correlated with the explanatory variables dependence is specified through the conditional expectation of the effects which is let to be non parametric we also present a gmm estimator for these models which is consistent and asymptotically normal for fixed t and large n we study its finite sample properties in an autoregressive model by means of monte carlo simulations finally as an empirical illustration we estimate a female labour force participation equation with predetermined children using psid data

optimal detection of a change in distribution abstract document discusses a stopping rule that is a limit of bayes rules derived then an almost minimax rule is presented

bounds on parameters in panel dynamic discrete choice models identification of dynamic nonlinear panel data models is an important and delicate problem in econometrics in this paper we provide insights that shed light on the identification of parameters of some commonly used models using these insights we are able to show through simple calculations that point identification often fails in these models on the other hand these calculations also suggest that the model restricts the parameter to lie in a region that is very small in many cases and the failure of point identification may therefore be of little practical importance in those cases although the emphasis is on identification our techniques are constructive in that they can easily form the basis for consistent estimates of the identified sets

quickest change detection and kullback leibler divergence for two state hidden markov models in this paper the quickest change detection problem is studied in two state hidden markov models hmm where the vector parameter theta of the hmm changes from theta 0 to theta 1 at some unknown time and one wants to detect the true change as quickly as possible while controlling the false alarm rate it turns out that the generalized likelihood ratio glr scheme while theoretically straightforward is generally computationally infeasible for the hmm to develop efficient but computationally simple schemes for the hmm we first discuss a subtlety in the recursive form of the generalized likelihood ratio glr scheme for the hmm then we show that the recursive cusum scheme proposed in fuh ann statist 2003 can be regarded as a quasi glr scheme for pseudo post change hypotheses with certain dependence structure between pre and postchange observations next we extend the quasi glr idea to propose recursive score schemes in the scenario when the postchange parameter theta 1 of the hmm involves a real valued nuisance parameter finally the kullback leibler kl divergence plays an essential role in the quickest change detection problem and many other fields however it is rather challenging to numerically compute it in hmms here we develop a non monte carlo method that computes the kl divergence of two state hmms via the underlying invariant probability measure which is characterized by the fredholm integral equation numerical study demonstrates an unusual property of the kl divergence for hmm that implies the severe effects of misspecifying the postchange parameter for the hmm

the asymptotic variance of semiparametric estimators this paper derives a general formula for the asymptotic variance of semiparametric estimators that accounts for the presence of nonparametric estimators of functions the general formula is specialized to show invariance of the asymptotic variance to the type of nonparametric estimator and to obtain correction terms for estimation of densities and mean square projections including conditional expectations regularity conditions for the validity of the formula are also given including primitive conditions for asymptotic normality when series estimators are present new examples considered include a semiparametric panel probit estimator and a series estimator of the average derivative copyright 1994 by the econometric society

identification and estimation of nonseparable single index models in panel data with correlated random effects the identification in a nonseparable single index models with correlated random effects is considered in panel data with a fixed number of time periods the identification assumption is based on the correlated random effects structure under this assumption the parameters of interest are identified up to a multiplicative constant and could be estimated by an average difference of derivatives estimator based on the local polynomial smoothing we suggest to linearly combine the estimators obtained for different orders of differences and derive the variance minimizing weighting scheme the asymptotic distribution of the proposed estimators is derived both for stationary and non stationary explanatory variables along with a test of the stationarity finally monte carlo experiments reveal finite sample properties of the proposed estimator

identification and n consistent estimation of a nonlinear panel data model with correlated unobserved effects abstract this paper investigates identification and root n consistent estimation of a class of single index panel data models in which the link function is unknown the unobserved individual effects may be correlated with all the explanatory variables and all the explanatory variables may be predetermined we propose two sets of sufficient conditions one in which link function is assumed to be strictly increasing and the other in which it is not we propose simple kernel based estimators for the models and derive consistency and asymptotic normality results for the proposed estimators finally we present results of two monte carlo studies of the estimators

relative entropy rate based multiple hidden markov model approximation this paper proposes a novel relative entropy rate rer based approach for multiple hmm mhmm approximation of a class of discrete time uncertain processes under different uncertainty assumptions the model design problem is posed either as a min max optimisation problem or stochastic minimization problem on the rer between joint laws describing the state and output processes rather than the more usual rer between output processes a suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the rer is reduced these filter consistency and convergence bounds are the first results characterizing multiple hmm approximation performance and suggest that joint rer concepts provide a useful model selection criteria the proposed model design process and mhmm filter are demonstrated on an important image processing dim target detection problem

an accurate sph modeling of viscous flows around bodies at low and moderate reynolds numbers abstract a weakly compressible sph scheme has been used to describe the evolution of viscous flows around blunt bodies at reynolds numbers ranging from 10 to 2400 the simulation of such a wide range rarely addressed to in the sph literature has been possible thanks to the use of a proper ghost fluid technique and to an accurate enforcement of the boundary conditions along the solid boundaries in this context a new numerical technique based on previous works by takeda et al 1994 48 marrone et al 2011 28 and de leffe et al 2011 16 has been proposed along with a new method for the evaluation of the global loads on bodies particular care has been taken to study the influence of the weakly compressibility assumption and of different ghost fluid techniques on the numerical results an in depth validation of the model has been performed by comparing the numerical outcome with experimental data from the literature and other numerical references the influence of the domain size has been discussed in order to avoid wall side effects and at the same time to limit the computational costs the convergence of the numerical solutions has been checked on both global and local quantities by choosing appropriate reynolds cell number

misspecified and asymptotically minimax robust quickest change detection we investigate the quickest detection of an unknown change in the distribution of a stochastic process generating independent and identically distributed observations we develop new bounds on the performance of misspecified cumulative sum cusum rules and pose minimax robust versions of the popular lorden and pollak criteria with polynomial or higher order moment detection delay penalties by exploiting our results for misspecified cusum rules we identify solutions to our robust quickest change detection problems in the asymptotic regime of few false alarms in contrast to previous robust quickest change detection treatments our asymptotic results hold under relaxed conditions on the uncertainty sets of possible prechange and postchange distributions we illustrate our results in simulations and apply them to the problem of detecting target manoeuvres in low signal to noise ratio settings i e dim target manoeuvre detection

fast free surface detection and level set function definition in sph solvers the present paper proposes a novel algorithm to detect the free surface in particle simulations both in two and three dimensions since the proposed algorithms are based on sph interpolations their implementation does not require complex geometrical procedures thus the free surface detection can be easily embedded in sph solvers without a significant increase of the cpu time throughout this procedure accurate normal vectors to the free surface are made available then it is possible to define a level set function algorithm which is presented in detail the latter allows in depth analyses of three dimensional free surface simulations by using standard visualization tools including internal features of the flow the algorithms proposed for detecting free surface particles and defining the level set function are validated on simple and complex two and three dimensional flow simulations the usefulness of the proposed procedures to post process and analyze complex flows are illustrated on realistic examples

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

profile likelihood and conditionally parametric models in this paper we outline a general approach to estimating the parametric component of a semiparametric model for the case of a scalar parametric component the method is based on the idea of first estimating a one dimensional subproblem of the original problem that is least favorable in the sense of stein the likelihood function for the scalar parameter along this estimated subproblem may be viewed as a generalization of the profile likelihood for the problem the scalar parameter is then estimated by maximizing this generalized profile likelihood this method of estimation is applied to a particular class of semiparametric models where it is shown that the resulting estimator is asymptotically efficient

semiparametric binary choice panel data models without strictly exogeneous regressors previous estimators of binary choice panel data models with fixed effects require strong parametric error asumptions strictly exogeneous regressors or both this is because nonlinearity of the model precludes the use of the moment conditions on differences based estimators that are generally employed for linear models without strictly exogeneous regressors based on the cross section binary choice estimator in lewbel 2000a we show how discrete choice panel data models with fixed effects can be estimated with only predetermined regressors the estimator is semiparametric in that the error distribution is not specified it allows for some general forms of heteroskedasticity and converges at rate root n this abstract was borrowed from another version of this item

minimax optimal control of stochastic uncertain systems with relative entropy constraints this paper considers a new class of discrete time stochastic uncertain systems in which the uncertainty is described by a constraint on the relative entropy between a nominal noise distribution and the perturbed noise distribution this uncertainty description is a natural extension to the case of stochastic uncertain systems of the sum quadratic constraint uncertainty description this paper solves problems of worst case robust performance analysis and output feedback minimax optimal controller synthesis in a general nonlinear setting specializing these results to the linear case leads to a minimax linear quadratic gaussian lqg optimal controller this controller is defined by riccati difference equations and a kalman filter like state equation the paper also shows that the minimax lqg problem will have a solution if and only if a corresponding h sup spl infin control problem has a solution a linear example is presented to illustrate the minimax lqg methodology

cross section and panel data estimators for nonseparable models with endogenous regressors we propose two new methods for estimating models with nonseparable errors and endogenous regressors the first method estimates a local average response one estimates the response of the conditional mean of the dependent variable to a change in the explanatory variable while conditioning on an external variable and then undoes the conditioning the second method estimates the nonseparable function and the joint distribution of the observable and unobservable explanatory variables an external variable is used to impose an equality restriction at two points of support on the conditional distribution of the unobservable random term given the regressor and the external variable our methods apply to cross sections but our lead examples involve panel data cases in which the choice of the external variable is guided by the assumption that the distribution of the unobservable variables is exchangeable in the values of the endogenous variable for members of a group

a regularized single phase lattice boltzmann method for free surface flows abstract in this paper a lattice boltzmann lb scheme for simulating free surface flows is investigated in which the hermite expansion based regularization zhang et al 2006 and the single phase free surface model korner et al 2005 are adopted it is pointed out that the original free surface model may encounter some conflicting situations when dealing with some extreme cases such as adjacent filled empty interface cells isolated interface cells and how to reconstruct distribution functions if too few non gas cells are available in the neighbourhood region an alternative reconstruction method based on the extrapolation and the gradient of macroscopic variables is then proposed to tackle these problems the proposed numerical framework is validated through several test cases from which it is shown that the regularization can largely improve the stability of lb simulations of violent free surface flows such as dam break flows in addition the proposed reconstruction method can help further reduce the spurious pressure noises in regularized lbm nevertheless a more dissipative behaviour has been observed in the viscous standing wave test case

modeling low reynolds number incompressible flows using sph the method of smoothed particle hydrodynamics sph is extended to model incompressible flows of low reynolds number for such flows modification of the standard sph formalism is required to minimize errors associated with the use of a quasi incompressible equation of state treatment of viscosity state equation kernel interpolation and boundary conditions are described simulations using the method show close agreement with series solutions for couette and poiseuille flows furthermore comparison with finite element solutions for flow past a regular lattice of cylinders shows close agreement for the velocity and pressure fields the sph results exhibit small pressure fluctuations near curved boundaries further improvements to the boundary conditions may be possible which will reduce these errors a similar method to that used here may permit the simulation of other flows at low reynolds numbers using sph further development will be needed for cases involving free surfaces or substantially different equations of state

the convergence of the sph method abstract in this paper we prove the convergence of a smooth particle flow to the solution of a regularized version of the euler equations describing a generic polytropic fluid this result combined with a stability property of the euler equations with respect to suitable regularizations result of the same authors of the present paper which will be published elsewhere allows to achieve the proof of the convergence of the so called smoothed particle hydrodynamics sph method