global uniqueness in an inverse problem for time fractional diffusion equations given m g a compact connected riemannian manifold of dimension d 2 with boundary m we consider an initial boundary value problem for a fractional diffusion equation on 0 t m t 0 with time fractional caputo derivative of order 0 1 1 2 we prove uniqueness in the inverse problem of determining the smooth manifold m g up to an isometry and various time independent smooth coefficients appearing in this equation from measurements of the solutions on a subset of m at fixed time in the flat case where m is a compact subset of rd two out the three coefficients density a conductivity and q potential appearing in the equation t u div a u qu 0 on 0 t m are recovered simultaneously

on continuous dependence for an inverse initial boundary value problem for the wave equation abstract we show that the bounded coefficient q x of the wave equation u tt u qu x t 0 t u x 0 u t x 0 0 x u v x x x t 0 t can be continuously determined by its neumann to dirichlet map in certain function spaces

a semi parametric censored regression estimator abstract this paper introduces a semi parametric method for estimating regression co underlying parent population of errors in censored the method is an example of the method of sieves and it provides simultaneous estimates of the regression coefficients and the density of the underlying parent population in the very simplest terms the underlying unknown density is approximated by a spline with mesh size approaching zero with the sample size the values of the density at the knots are then added to the list of the usual unknown parameters in a censored regression model e g the regression coefficients and scale parameter a quasi likelihood function using the approximate spline density is then maximized over all the parameters mentioned above the method is shown to result in strongly consistent parameter estimates

orthogonality conditions for tobit models with fixed effects and lagged dependent variables abstract this paper presents orthogonality conditions for censored regression models with fixed effects and lagged dependent variables the orthogonality conditions can be used to construct method of moments estimators of the parameters of the model nonlinear fixed effects models are usually estimated by maximum likelihood with the fixed effects treated as parameters to be estimated monte carlo results indicate that in a tobit model with fixed effects and lagged dependent variables the maximum likelihood estimator of the effect of the lagged dependent variable performs poorly the method of moments estimator based on the orthogonality conditions presented here however performs quite well

symmetrically trimmed least squares estimation for tobit models this paper proposes alternatives to maximum likelihood estimation of the censored and truncated regression models known to economists as tobit models the proposed estimators are based upon symmetric censoring or truncation of the upper tail of the distribution of the dependent variable unlike methods based on the assumption of identically distributed gaussian errors the estimators are semiparametric in the sense that they are consistent and asymptotically normal for a wide class of symmetric error distributions with heteroskedasticity of unknown form the paper gives the regularity conditions and proofs of these large sample properties demonstrates how to construct consistent estimators of the asymptotic covariance matrices and presents the results of a simulation study for the censored case extensions and limitations of the approach are also considered

some aspects of measurement error in a censored regression model abstract when a linear model with a censored dependent variable is written as a regression model the model is nonlinear in the explanatory variables this means that when the explanatory variables are measured with error estimation by instrumental variables will not lead to consistent estimates of the parameters my approach to the problem involves assuming that the variables measured with error can be represented by a reduced form equation and estimating the parameters of the censored regression model by the least absolute deviations estimator this estimator is based on the median of the distribution of the dependent variable rather than the mean and the median is not affected by censoring in the lower tail hence the estimator is consistent under general conditions i also consider the problem of testing for the presence of measurement error

partition search for non binary constraint satisfaction previous algorithms for unrestricted constraint satisfaction use reduction search which inferentially removes values from domains in order to prune the backtrack search tree this paper introduces partition search which uses an efficient join mechanism instead of removing values from domains analytical prediction of quantitative performance of partition search appears to be intractable and evaluation therefore has to be by experimental comparison with reduction search algorithms that represent the state of the art instead of working only with available reduction search algorithms this paper introduces enhancements such as semijoin reduction preprocessing using bloom filtering

uniqueness in the cauchy problem for operators with partially holomorphic coefficients the problem of the uniqueness in the cauchy problem for linear di erential operators has been widely investigated during the last years see z for references it is now well understood in the analytic framework with holmgren s theorem where uniqueness always holds at least for non characteristic surfaces and in the c1 case with hoe rmander s theorem h1 iv chap 28 where the uniqueness is governed by principal normality and pseudo convexity the purpose of this work is to ll the gap between these two theorems by considering operators with c1 and partly analytic coe cients in particular one of our results will contain both the theorems mentioned above let us be more precise let na nb be two non negative integers with n na nb 1 we shall set rn rna rnb and for x or n in rn x xa xb n na nb let p p x d p xa xb dxa dxb be a linear di erential operator of arbitrary order m with principal symbol pm we shall assume that

pairwise difference estimators of censored and truncated regression models abstract this paper proposes a class of estimators of the semiparametric censored regression model under the assumption that the error terms are i i d and independent of the covariates the estimators exploit the fact that for a pair of observations a particular transformation of the censored variables depending upon the parameter vector and both covariate vectors will be identically distributed therefore the difference in the transformed dependent variables will be symmetrically distributed around zero when the transformation is evaluated at the true parameter vector an analogous class of estimators for the truncated regression model includes the bhattacharya chernoff and yang 1983 estimator as a special case the estimators are defined as minimizers of u processes so the large sample theory for classical m estimators is extended to this case conditions are given to ensure root n consistency and asymptotic normality of the estimators and a small scale simulation study for the censored regression estimator suggest that it will be well behaved in finite samples

semiparametric and nonparametric estimation of sample selection models under symmetry this paper considers the semiparametric estimation of binary choice sample selection models under a joint symmetry assumption our approaches overcome various drawbacks associated with existing estimators in particular our method provides root n consistent estimators for both the intercept and slope parameters of the outcome equation in a heteroscedastic framework without the usual cross equation exclusion restriction or parametric specification for the error distribution and or the form of heteroscedasticity our two step estimators are shown to be consistent and asymptotically normal a monte carlo simulation study indicates the usefulness of our approaches

stable recovery of time dependent coefficient from arbitrary measurements for wave equation abstract this paper concerns the stability in the inverse problem of determining the time dependent coefficient in the wave equation from partial knowledge of the dirichlet to neumann map at the boundary of a bounded domain of interest we first establish a stability estimate in the determination of the time dependent coefficient from partial boundary observations then we prove that by enlarging the set of data the unknown coefficient can be stably determined in larger regions

efficient estimation of limited dependent variable models with endogenous explanatory variables abstract this paper discusses asymptotically efficient estimation of the parameters of limited dependent variable models with endogenous explanatory variables general results on asymptotic efficiency of two stage and amemiya gls estimators are derived and used to obtain a simple asymptotically efficient estimator of the structural coefficients this estimator can be calculated by applying gls to estimates of the reduced form coefficients that are obtained by using reduced form residuals as additional explanatory variables it is also shown that it is possible to obtain asymptotically efficient estimators of the other coefficients by a modified minimum chi square method

a pseudo r2 measure for limited and qualitative dependent variable models abstract a pseudo r 2 measure for limited and qualitative dependent variable models is proposed a consistent estimator of the variance of the disturbance term is also suggested the pseudo r 2 measure is shown to have the same asymptotic limit as the conventional r 2 the measure is adapted to the logit model in an empirical example of female car ownership modeling the pseudo r 2 is applicable to a wide set of limited and qualitative dependent variable models and offers a valuable tool for model evaluation

rank estimation of a location parameter in the binary choice model abstract this paper proposes a rank based estimator for a location parameter in the binary choice model under a monotonic index and symmetry condition given an initial n consistent estimator for the slope parameter the estimator converges at the usual parametric rate compared with existing estimators no nonparametric smoothing is needed here a small monte carlo study illustrates the usefulness of the estimator we also point out that the location and slope parameters can be jointly estimated

estimation of type 3 tobit models using symmetric trimming and pairwise comparisons abstract this paper considers estimation of the semiparametric type 3 tobit model we construct two step estimators that use the ideas of symmetric trimming and pairwise comparisons which have been proposed for the censored regression model by powell 1986 and honore and powell 1994 the estimators are consistent and asymptotically normal under two identifying assumptions conditional symmetry and independence between the errors and regressors the ideas and techniques are also implementable in other selection models that are variations of the basic type 3 tobit model we demonstrate this by applying the proposed method in estimating an empirical model considered by udry 1994

stability in the determination of a time dependent coefficient for wave equations from partial data let be a c2 bounded domain of rn n 2 and fix q 0 t with t 0 we consider the stability in the inverse problem of determining a time dependent coefficient of order zero q appearing in a dirichlet initial boundary value problem for a wave equation t2u xu q t x u 0 in q from partial observations on q the observation is given by a boundary operator associated to the wave equation using suitable geometric optics solutions and carleman estimates we prove a stability estimate in the determination of q from the boundary operator

a sufficient condition for backtrack bounded search backtrack search is often used to solve constraint satisfaction problems a relationship involving the structure of the constraints is described that provides a bound on the backtracking required to advance deeper into the backtrack tree this analysis leads to upper bounds on the effort required for solution of a class of constraint satisfaction problems the solutions involve a combination of relaxation preprocessing and backtrack search the bounds are expressed in terms of the structure of the constraint connections specifically the effort is shown to have a bound exponential in the size of the largest biconnected component of the constraint graph as opposed to the size of the graph as a whole

on forward checking for non binary constraint satisfaction solving non binary constraint satisfaction problems a crucial challenge today can be tackled in two different ways translating the non binary problem into an equivalent binary one or extending binary search algorithms to solve directly the original problem the latter option raises some issues when we want to extend definitions written for the binary case this paper focuses on the well known forward checking algorithm and shows that it can be generalized to several non binary versions all fitting its binary definition the classical non binary version proposed by van hentenryck is only one of these generalizations

the effect of truncation on the identifiability of regression coefficients abstract when data are truncated and hence partly unobservable parameters identifiable under the complete observation may not be identifiable a class of distributions is characterized under which regression coefficients are unidentifiable by truncation of a dependent variable in the regression model in a class of strongly unimodal densities regression coefficients are shown to be unidentifiable only when the underlying distribution is proportional to the exponential distribution in support of the truncated observation the exponential double exponential and huber s least informative distribution for a location parameter are apparent examples of the distributions under which the unidentifiability can occur in a more restrictive class of symmetric and strongly unimodal densities the unidentifiability arises only when the error distribution has exponential tails and the degree of truncation is at least 50 the likelihood function becomes completely flat with respect to the regression coefficients and

on minimal constraint networks in a minimal binary constraint network every tuple of a constraint relation can be extended to a solution the tractability or intractability of computing a solution to such a minimal network was a long standing open question dechter conjectured this computation problem to be np hard we prove this conjecture we also prove a conjecture by dechter and pearl stating that for k 2 it is np hard to decide whether a single constraint can be decomposed into an equivalent k ary constraint network we show that this holds even in case of bi valued constraints where k 3 which proves another conjecture of dechter and pearl finally we establish the tractability frontier for this problem with respect to the domain cardinality and the parameter k

method of moments estimation and identifiability of semiparametric nonlinear errors in variables models abstract this paper deals with a nonlinear errors in variables model where the distributions of the unobserved predictor variables and of the measurement errors are nonparametric using the instrumental variable approach we propose method of moments estimators for the unknown parameters and simulation based estimators to overcome the possible computational difficulty of minimizing an objective function which involves multiple integrals both estimators are consistent and asymptotically normally distributed under fairly general regularity conditions moreover root n consistent semiparametric estimators and a rank condition for model identifiability are derived using the combined methods of the nonparametric technique and fourier deconvolution

computing and restoring global inverse consistency in interactive constraint satisfaction some applications require the interactive resolution of a constraint problem by a human user in such cases it is highly desirable that the person who interactively solves the problem is not given the choice to select values that do not lead to solutions we call this property global inverse consistency existing systems simulate this either by maintaining arc consistency after each assignment performed by the user or by compiling offline the problem as a multi valued decision diagram in this article we define several questions related to global inverse consistency and analyze their complexity despite their theoretical intractability we propose several algorithms for enforcing and restoring global inverse consistency and we show that the best version is efficient enough to be used in an interactive setting on several configuration and design problems

adaptive estimation in time series regression models this work develops adaptive estimators for a linear regression model with serially correlated errors we show that these results continue to hold when the order of the arma process characterizing the errors is unknown the finite sample results are promising indicating that substantial efficiency gains may be possible for samples as small as 50 observations we use these estimators to investigate the behavior of the forward foreign exchange market

instrumental variable estimator for the nonlinear errors in variables model abstract estimation for the nonlinear errors in variables model is considered it isassumed that additional information is available in the form of observations on instrumental variables an estimation procedure is presented for the parameters of the model asymptotic properties of the estimator are investigated

distribution free estimation of the random coefficient dummy endogenous variable model this paper considers the estimation of the random coefficient dummy endogenous variable model under a symmetry and index condition for the error distribution we follow a two step approach the selection equation is estimated first in the second step an instrumental variables estimator is proposed for the ourcome equation through a pairwise comparison method our approach can be readily extended to the general switching regression model in constrast to the existing literature on sample selection models the exclusion restriction is not needed here for model identification furthermore the intercept term for the outcome equation can be estimated at the usual parametric rate

stability estimate for an inverse wave equation and a multidimensional borg levinson theorem abstract we consider the stability in an inverse problem of determining the potential q entering the wave equation t 2 u u q x u 0 in a bounded smooth domain of r d from boundary observations the observation is given by a hyperbolic dynamic dirichlet to neumann map associated to a wave equation we prove a log type stability estimate in determining q from a partial dirichlet to neumann map provided that q is a priori known in a neighbourhood of the boundary of the spatial domain and satisfies an additional condition next we use this result to establish a stability estimate related to the multidimensional borg levinson theorem

least absolute deviations estimation for the censored regression model this paper proposes an alternative to maximum likelihood estimation of the parameters of the censored regression or censored tobit model the proposed estimator is a generalization of least absolute deviations estimation for the standard linear model and unlike estimation methods based on the assumption of normally distributed error terms the estimator is consistent and asymptotically normal for a wide class of error distributions and is also robust to heteroscedasticity the paper gives the regularity conditions and proofs of these large sample results and proposes classes of consistent estimators of the asymptotic covariance matrix for both homoscedastic and heteroscedastic disturbances

generalized method of moments specification testing abstract this paper analyzes the asymptotic power properties of specification tests which are based on a finite set of moment conditions it shows that any such test may fail against general misspecification that causes estimator inconsistency the mutual asymptotic equivalence of maximal degree of freedom tests is shown and the form of optimal tests against specific forms of misspecification is derived applications to testing for exogeneity of a set of instrumental variables are presented

censored normal regression with measurement error on the dependent variable when zero mean measurement error is added to the dependent variable for the nonlimit observations of the censored normal regression model the conventional maximum likelihood estimator tobit is inconsistent correct maximum likelihood estimation appears to be computationally difficult under various specifications for the distribution of the measurement error estimators based on either the expectation function or the conditional expectation function for uncensored observations remain consistent in the presence of measurement error eight such estimators are examined the results of a numerical experiment suggest that several of these estimators are substantially more efficient than the conventional maximum likelihood estimator when measurement error exists and that they also will do reasonably well when it does not

stability estimate for a multidimensional inverse spectral problem with partial spectral data abstract in bellassoued choulli and yamamoto 2009 4 we proved a log log type stability estimate for a multidimensional inverse spectral problem with partial spectral data for a schrodinger operator provided that the potential is known in a small neighbourhood of the boundary of the domain in the present paper we discuss the same inverse problem we show a log type stability estimate under an additional condition on potentials in terms of their x ray transform in proving our result we follow the same method as in alessandrini and sylvester 1990 1 and bellassoued choulli and yamamoto 2009 4 that is we relate the stability estimate for our inverse spectral problem to a stability estimate for an inverse problem consisting in the determination of the potential in a wave equation from a local dirichlet to neumann map dn map in short

a nonparametric approach to the truncated regression problem abstract a description is given of a new method of estimating the regression parameters in the linear regression model from data where the dependent variable is subject to truncation the residual distribution is allowed to be unspecified the method is iterative and involves estimation of the residual distribution under the truncated sampling scheme the technique can be interpreted as an iterative bias adjustment of the observations in order to correct the regression relationship in the sampled population to match that of the model a simulation study compares the performance of various estimators including one suggested by bhattacharya chernoff and yang 1983 this truncation regression problem arises in many contexts of scientific and social research in economics tobin 1958 analyzed household expenditure on durable goods using a regression model that took account of the fact that the expenditure is always nonnegative a more general situation was studied by hausman and wise 1976 1977 in conn

two step estimation of semiparametric censored regression models root n consistent estimators of the regression coecients in the linear censored regression model under conditional quantile restrictions on the error terms were pro posed by powell journal of econometrics 25 1984 303 325 32 1986a 143 155 while those estimators have desirable asymptotic properties under weak regularity conditions simulation studies have shown these estimators to exhibit a small sam ple bias in the opposite direction of the least squares bias for censored data this paper introduces two step estimators for these models which minimize convex objec tive functions and are designed to overcome thisnite sample bias the paper gives regularity conditions under which the proposed two step estimators are consistent and asymptotically normal a monte carlo study compares thenite sample behavior of the proposed methods with their one step counterparts 2001 elsevier science s a all rights reserved

an inverse problem of finding two time dependent coefficients in second order hyperbolic equations from dirichlet to neumann map abstract in the present paper we consider a non self adjoint hyperbolic operator with a vector field and an electric potential that depend not only on the space variable but also on the time variable more precisely we attempt to stably and simultaneously retrieve the real valued velocity field and the real valued potential from the knowledge of neumann measurements performed on the whole boundary of the domain we establish in dimension n greater than two stability estimates for the problem under consideration thereafter by enlarging the set of data we show that the unknown terms can be stably retrieved in larger regions including the whole domain the proof of the main results is mainly based on the reduction of the inverse problem under investigation to an equivalent and classic inverse problem for an electro magnetic wave equation

stability estimate for the hyperbolic inverse boundary value problem by local dirichlet to neumann map abstract in this paper we consider the stability of the inverse problem of determining a function q x in a wave equation t 2 u u q x u 0 in a bounded smooth domain in r n from boundary observations this information is enclosed in the hyperbolic dynamic dirichlet to neumann map associated to the solutions to the wave equation we prove in the case of n 2 that q x is uniquely determined by the range restricted to a subboundary of the dirichlet to neumann map whose stability is a type of double logarithm

consistency restoriation and explanations in dynamic csps application to configuration most of the algorithms developed within the constraint satisfaction problem csp framework cannot be used as such to solve interactive decision support problems like product configuration indeed in such problems the user is in charge of assigning values to variables global consistency maintaining is only one among several functionalities that should be offered by a csp based platform in order to help the user in her task other important functionalities include providing explanations for some user s choices and ways to restore consistency

specification tests for distributional assumptions in the tobit model abstract the recent development of distribution robust estimators for the censored regression model allows for the construction of specification tests of the normality and homoskedasticity assumption of the tobit model this paper considers hausman 1978 specification tests for the tobit model that are based on powell s 1986 symmetrically censored least squares estimator testing for models with explanatory variables that are endogeneous is considered as well as the simpler case where the explanatory variables are exogenous testing of the maintained conditional symmetry assumption of the symmetrically censored least squares estimator is also considered the tests are implemented in an empirical example of a model of female labor supply

estimation of censored linear errors in variables models this paper deals with a linear errors in variables model where the dependent variable is censored a two step procedure is proposed to estimate the model and the corresponding asymptotic covariance matrices are derived the framework covers the usual error free tobit model as a special case it is shown that under normality and a certain identifying condition this model can be uniquely reduced to an error free censored regression model and hence the existing estimators for the tobit model can be used to obtain estimators for this model in particular the maximum likelihood estimator is derived in this way the small sample behavior of the two estimators and their sensitivities to misspecified identifying information are studied through monte carlo simulations

estimation of nonlinear models with measurement error this paper presents a solution to an important econometric problem namely the root n consistent estimation of nonlinear models with measurement errors in the explanatory variables when one repeated observation of each mismeasured regressor is available while a root n consistent estimator has been derived for polynomial specifications see hausman ichimura newey and powell 1991 such an estimator for general nonlinear specifications has so far not been available using the additional information provided by the repeated observation the suggested estimator separates the measurement error from the true value of the regressors thanks to a useful property of the fourier transform the fourier transform converts the integral equations that relate the distribution of the unobserved true variables to the observed variables measured with error into algebraic equations the solution to these equations yields enough information to identify arbitrary moments of the true unobserved variables the value of these moments can then be used to construct any estimator that can be written in terms of moments including traditional linear and nonlinear least squares estimators or general extremum estimators the proposed estimator is shown to admit a representation in terms of an influence function thus establishing its root n consistency and asymptotic normality monte carlo evidence and an application to engel curve estimation illustrate the usefulness of this new approach

determination of coefficients for a dissipative wave equation via boundary measurements in this paper we consider the inverse problem of recovering the viscosity coefficient in a dissipative wave equation via boundary measurements we obtain stability estimates by considering all possible measurements implemented on the boundary we also prove that the viscosity coefficient is uniquely determined by a finite number of measurements on the boundary provided that it belongs to a given finite dimensional vector space

from local to global consistency abstract in reasoning tasks involving the maintenance of consistent databases so called qqconstraint networks q q it is customary to enforce local consistency conditions in order to simplify the subsequent construction of a globally coherent model of the data in this paper we present a relationship between the sizes of the variables domains the constraints arity and the level of local consistency sufficient to ensure global consistency based on these parameters a new tractability classification of constraint networks is presented we also show based on this relationship that any relation on bi valued variables which is not representable by a network of binary constraints cannot be represented by networks with any number of hidden variables

stable determination outside a cloaking region of two time dependent coefficients in an hyperbolic equation from dirichlet to neumann map abstract in this paper we treat the inverse problem of determining two time dependent coefficients appearing in a dissipative wave equation from measured neumann boundary observations we establish in dimension n 2 stability estimates with respect to the dirichlet to neumann map of these coefficients provided that they are known outside cloaking regions moreover we prove that they can be stably recovered in larger subsets of the domain by enlarging the set of data

logarithmic stability in determining two coefficients in a dissipative wave equation extensions to clamped euler bernoulli beam and heat equations abstract we are concerned with the inverse problem of determining both the potential and the damping coefficient in a dissipative wave equation from boundary measurements we establish stability estimates of logarithmic type when the measurements are given by the operator who maps the initial condition to neumann boundary trace of the solution of the corresponding initial boundary value problem we build a method combining an observability inequality together with a spectral decomposition we also apply this method to a clamped euler bernoulli beam equation finally we indicate how the present approach can be adapted to a heat equation