root n consistent semiparametric regression on considere une variable aleatoire x z dans 7b r p 7b r q on construit un estimateur generalisant l estimateur des moindres carres ordinaires en inserant des estimateurs non parametriques de la regression dans la projection orthogonale non lineaire sur z

optimal rates of convergence for deconvolving a density abstract suppose that the sum of two independent random variables x and z is observed where z denotes measurement error and has a known distribution and where the unknown density f of x is to be estimated one application is the estimation of a prior density for a sequence of location parameters a second application arises in the errors in variables problem for nonlinear and generalized linear models when one attempts to model the distribution of the true but unobservable covariates this article shows that if z is normally distributed and f has k bounded derivatives then the fastest attainable convergence rate of any nonparametric estimator of f is only log n k 2 therefore deconvolution with normal errors may not be a practical proposition other error distributions are also treated stefanski carroll 1987a estimators achieve the optimal rates the results given have versions for multiplicative errors where they imply that even optimal rates are exceptionally slow

use of the method of manufactured solutions for the verification of conjugate heat transfer solvers this paper demonstrates the use of the method of manufactured solutions to verify the implementation of tightly coupled conjugate heat transfer for fluid solid solvers the interface conditions in the prescribed manufactured solutions were implemented to mimic real effects such as no slip and temperature heat flux match between the solid and fluid domains the newly developed solid heat transfer solver was verified in standalone mode using this prescribed manufactured solution and was found to have no apparent coding errors our pre existing in house compressible fluid solver eilmer was used to demonstrate the conjugate heat transfer implementation both the fluid and solid solvers showed an expected spatial order of convergence of 2 0 in the standalone mode the coupled conjugate heat transfer mode also showed no coding errors and demonstrated that the spatial order of convergence was again 2 0 the one sided spatial discretisation utilised to enforce the tight coupling for the interface conditions were effectively equivalent to a central difference hence the overall spatial order of the error convergence for the entire domain including the interface was 2 0 the method prescribed in this work can be extended for verification of other conjugate heat transfer solvers in particular for compressible flow scenarios where analytical solutions may not be readily available

testing for unobserved heterogeneity in exponential and weibull duration models abstract we examine the use of the likelihood ratio lr statistic to test for unobserved heterogeneity in duration models based on mixtures of exponential or weibull distributions we consider both the uncensored and censored duration cases the asymptotic null distribution of the lr test statistic is not the standard chi square as the standard regularity conditions do not hold instead there is a nuisance parameter identified only under the alternative and a null parameter value on the boundary of the parameter space as in cho and white 2007a we accommodate these and provide methods delivering consistent asymptotic critical values we conduct a number of monte carlo simulations comparing the level and power of the lr test statistic to an information matrix im test due to chesher 1984 and lagrange multiplier lm tests of kiefer 1985 and sharma 1987 our simulations show that the lr test statistic generally outperforms the im and lm tests we also revisit the work of van den berg and ridder 1998 on unemployment durations and of ghysels et al 2004 on interarrival times between stock trades and as it turns out affirm their original informal inferences

direct semiparametric estimation of single index models with discrete covariates abstract others have developed average derivative estimators of the parameter in the model e y x x g x where g is an unknown function and x is a random vector these estimators are noniterative and easy to compute but require that x be continuously distributed this article develops a noniterative easily computed estimator of for models in which some components of x are discrete the estimator is n consistent and asymptotically normal an application to data on product innovation by german manufacturers illustrates the estimator s usefulness

the analysis of transformed data abstract recently it was suggested bickel and doksum 1981 that when data are used to select a transformation the post transformation analysis of those data may need to be modified considerably from standard form so as to allow for the selection we argue that common sense and the work of box and cox 1964 point to a contrary conclusion our argument is based on considerations of parameter interpretation and subsequent bayesian analysis within the context of fitting normal error linear models numerical examples are used to illustrate the main points

semiparametric estimation of a proportional hazard model with unobserved heterogeneity the proportional hazard model with unobserved heterogeneity gives the hazard function of a random variable conditional on covariates and a second random variable representing unobserved heterogeneity this paper shows how to estimate the baseline hazard function and the distribution of the unobserved heterogeneity nonparametrically the baseline hazard function and heterogeneity distribution are assumed to satisfy smoothness conditions but are not assumed to belong to known finite dimensional parametric families existing estimators assume that the baseline hazard function or heterogeneity distribution belongs to a known parametric family thus the estimators presented here are more general than existing ones

the limiting distribution of the maximum rank correlation estimator han s maximum rank correlation mrc estimator is shown to be 4 consistent and asymptotically normal the proof rests on a general method for determining the asymptotic distribution of a maximization estimator a simple u statistic decomposition and a uniform bound for degenerate u processes a consistent estimator of the asymptotic covariance matrix is provided along with a result giving the explicit form of this matrix for any model within the scope of the mrc estimator the latter result is applied to the binary choice model and it is found that the mrc estimator does not achieve the semiparametric efficiency bound

a new relative perturbation theorem for singular subspaces abstract this note addresses the sensitivity of singular subspaces of a matrix under relative perturbations it employs a new technique of separating a multiplicative perturbation d into two components one is the distance of a scalar multiple of d to the nearest unitary matrix q and the other is the distance of q to the identity consequently the new bounds reflect the intrinsic differences in how left and right multiplicative perturbations affect left and right singular subspaces

optimal solutions of numerical interface conditions in fluid structure thermal analysis abstract this paper presents major and new results in the numerical treatment of conjugate heat transfer problems the stability analysis of a 1d implicit model problem for heat transfer between a fluid and a solid is performed in the traditional finite volume fluid finite element solid configuration the interface stability study is carried out according to the godunov ryabenkii theory normal mode analysis two interface boundary conditions are studied first the commonly used dirichlet robin algorithm is described in detail and to the best of our knowledge the exact expression of an optimal coupling coefficient is formulated for the first time it is shown that this optimal coefficient is the best choice in terms of stability and convergence rate the effect and impact on stability of various solid data are also discussed in the last section of this paper the same stability analysis is carried out for a general robin robin interface condition and an optimal relationship between the two coupling parameters is also provided no stability restrictions are introduced by these optimal interface treatments and some noteworthy expressions are provided

distribution free maximum likelihood estimator of the binary choice model1 is a given function of the exogenous variables z and unknown parameters 9 representing the systematic component of the utility difference and f is the distribution function of the random component of the utility difference this paper describes a method of estimating the parameters 9 without assuming any functional form for the distribution function f and proves that this estimator is consistent f is also consistently estimated the method uses maximum likelihood estimation in which the likelihood is maximized not only over the parameter 9 but also over a space which contains all distribution functions

identification of a competing risks model with unknown transformations of latent failure times this paper is concerned with identification of a competing risks model with unknown transformations of latent failure times the model includes as special cases competing risks versions of proportional hazards mixed proportional hazards and accelerated failure time models it is shown that covariate effects on latent failure times cause specific link functions and the joint survivor function of the disturbance terms can be identified without relying on modelling the dependence between latent failure times parametrically nor using an exclusion restriction among covariates as a result the paper provides an identification result about the joint survivor function of the latent failure times conditional on covariates

the unobserved heterogeneity distribution in duration analysis jabbringgfeweb vu nl gberggfeweb vu nl summary in a large class of hazard models with proportional unobserved heterogeneity the distribution of the heterogeneity among survivors converges to a gamma distribution this convergence is often rapid we derive this result as a general result for exponential mixtures and explore its implications for the specification and empirical analysis of univariate and multivariate duration models

a single stable scheme for steady conjugate heat transfer problems abstract the goal of this paper is to propose a single interface treatment based on the dirichlet robin interface condition to deal with all steady cht scenarios these scenarios depend on the so called numerical biot number that controls the stability process and the optimal coefficient that ensures in theory unconditional stability it is shown that this coefficient is closely related to fundamental thermal quantities for very large thermal fluid solid interactions the dirichlet robin condition may result in profound stability issues a thorough examination of the stability behavior has highlighted a narrow and slow varying stable zone located around the optimal coefficient this allows us to determine coupling coefficients valid in any case and the reasonable value of these coefficients avoids significantly impairing the accuracy of cht solutions a flat plate partially protected by a thermal barrier coating is presented as a test case

a simple nonparametric approach to estimating the distribution of random coefficients in structural models abstract we explore least squares and likelihood nonparametric mixtures estimators of the joint distribution of random coefficients in structural models the estimators fix a grid of heterogeneous parameters and estimate only the weights on the grid points an approach that is computationally attractive compared to alternative nonparametric estimators we provide conditions under which the estimated distribution function converges to the true distribution in the weak topology on the space of distributions we verify most of the consistency conditions for three discrete choice models we also derive the convergence rates of the least squares nonparametric mixtures estimator under additional restrictions we perform a monte carlo study on a dynamic programming model

a composite grid solver for conjugate heat transfer in fluid structure systems we describe a numerical method for modeling temperature dependent fluid flow coupled to heat transfer in solids this approach to conjugate heat transfer can be used to compute transient and steady state solutions to a wide range of fluid solid systems in complex two and three dimensional geometry fluids are modeled with the temperature dependent incompressible navier stokes equations using the boussinesq approximation solids with heat transfer are modeled with the heat equation appropriate interface equations are applied to couple the solutions across different domains the computational region is divided into a number of sub domains corresponding to fluid domains and solid domains there may be multiple fluid domains and multiple solid domains each fluid or solid sub domain is discretized with an overlapping grid the entire region is associated with a composite grid which is the union of the overlapping grids for the sub domains a different physics solver fluid solver or solid solver is associated with each sub domain a higher level multi domain solver manages the entire solution process we propose and analyze some centered discrete approximations to the interface equations that have some desirable stability properties the coupled interface equations may be solved directly when using explicit time stepping methods in the sub domains resulting in a strongly coupled approach the stability of the interface treatment in this case is independent of the relative sizes of the material properties in the two domains with the time step only depending on the usual von neumann conditions for each sub domain for implicit time stepping methods we solve the interface equations in a weakly coupled fashion to avoid forming a coupled implicit system across all sub domains the convergence of this approach does depend on the relative sizes of the thermal conductivities and diffusivities we analyze different iteration strategies for solving these implicit equations including the use of mixed robin approximations at the interface numerical results are presented to illustrate the method the accuracy of the technique is verified using the method of analytic solutions and by computing the solution to some heat exchanger problems where the exact solution is known the technique is also applied to the modeling of an inertial confinement fusion hohlraum target and the flow of coolant past an hexagonal array of heated fuel rods the multi domain solver runs in parallel on distributed memory computers and some parallel results are provided

non parametric analysis of a generalized regression model the maximum rank correlation estimator the paper considers estimation of a model yi d f x i 0 ui where the composite transformation d f is only specified that d r r is non degenerate monotonic and f r2 r is strictly monotonic in each of its variables the paper thus generalizes standard data analysis which assumes that the functional form of d f is known and additive the estimator which it proposes is the maximum rank correlation estimator which is non parametric in the functional form of d f and non parametric in the distribution of the error terms ui the estimator is shown to be strongly consistent for the parameters 0 up to a scale coefficient

smooth optimum kernel estimators of densities regression curves and modes several criteria concerning the choice of kernels for the nonparametric estimation of functions and their derivatives are discussed a specific optimality criterion is described which applies to kernels of compact support and of different orders of smoothness the solutions of the corresponding variational problems are explicitly given many kernels discussed previously are obtained as special cases

simple estimation of a duration model with unobserved heterogeneity this paper presents a simple estimator of the shape parameter in a weibull duration model with unobserved heterogeneity the estimator is consistent and asymptotically normal under mild conditions and a consistent estimator of the asymptotic variance is available a monte carlo study indicates that the asymptotic distribution of the estimator provides a good approximation to the finite sample distribution the estimation strategy can be extended to a model with regressors and to a log logistic model with unobserved heterogeneity the advantages of the estimator are that it is easy to calculate and that its asymptotic distribution can be derived copyright 1990 by the econometric society

fluid structure partitioned procedures based on robin transmission conditions in this article we design new partitioned procedures for fluid structure interaction problems based on robin type transmission conditions the choice of the coefficient in the robin conditions is justified via simplified models the strategy is effective whenever an incompressible fluid interacts with a relatively thin membrane as in hemodynamics applications we analyze theoretically the new iterative procedures on a model problem which represents a simplified blood vessel system in particular the robin neumann scheme exhibits enhanced convergence properties with respect to the existing partitioned procedures the theoretical results are checked using numerical experimentation

relative perturbation theory iv sin 2θ theorems abstract the double angle theorems of davis and kahan bound the change in an invariant subspace when a hermitian matrix a is subject to an additive perturbation a a a a this paper supplies analogous results when a is subject to a congruential or multiplicative perturbation a a d ad the relative gaps that appear in the bounds involve the spectrum of only one matrix either a or a in contrast to the gaps that appear in the single angle bounds the double angle theorems do not directly bound the difference between the old invariant subspace s and the new one s but instead bound the difference between s and its reflection j s where the mirror is s and j reverses s the orthogonal complement of s the double angle bounds are proportional to the departure from the identity and from orthogonality of the matrix d def d 1 jdj note that d is invariant under the transformation d d for 0 whereas the single angle theorems give bounds proportional to d s departure from the identity and from orthogonality the corresponding results for the singular value problem when a nonsquare matrix b is perturbed to b d 1 bd 2 are also presented

the singularity of the information matrix of the mixed proportional hazard model this paper presents new identification conditions for the mixed proportional hazard model in particular the baseline hazard is assumed to be bounded away from 0 and near t 0 these conditions ensure that the information matrix is nonsingular the paper also presents an estimator for the mixed proportional hazard model that converges at rate n 1 2

a stable and high order accurate conjugate heat transfer problem this paper analyzes well posedness and stability of a conjugate heat transfer problem in one space dimension we study a model problem for heat transfer between a fluid and a solid the energy method is used to derive boundary and interface conditions that make the continuous problem well posed and the semi discrete problem stable the numerical scheme is implemented using 2nd 3rd and 4th order finite difference operators on summation by parts sbp form the boundary and interface conditions are implemented weakly we investigate the spectrum of the spatial discretization to determine which type of coupling that gives attractive convergence properties the rate of convergence is verified using the method of manufactured solutions

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

absolute and relative perturbation bounds for invariant subspaces of matrices absolute and relative perturbation bounds are derived for angles between invariant subspaces of complex square matrices in the two norm and in the frobenius norm the absolute bounds can be considered extensions of davis and kahan s sin theorem to general matrices and invariant subspaces of any dimension the relative bounds are the most general relative bounds for invariant subspaces because they place no restrictions on the matrix or the perturbation when the perturbed subspace has dimension one the relative bound is implied by the absolute bound

heterogeneity and selection in dynamic panel data abstract the data generating process dgp for generic dynamic panel data consists of a law of state dynamics g a selection or attrition rule h and an initial condition f i study nonparametric identifiability of this complete dgp g h f using short unbalanced panel data allowing for nonseparability between observed states and unobserved heterogeneity in each of g h and f for t 3 the dgp is identified by using a proxy variable for t 6 the three additional periods construct a proxy and thus the dgp is identified without an auxiliary variable

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

two stage least absolute deviations estimators1 in this paper the method of least absolute deviations is applied to the estimation of the parameters of a structural equation in the simultaneous equations model a class of estimators called two stage least absolute deviations estimators is defined their asymptotic properties are derived and the problem of finding the optimal member of the class is considered in this paper we apply the method of least absolute deviations to the estimation of the parameters of a structural equation in the simultaneous equations model we define a class of estimators called two stage least absolute deviations estimators 2slad and derive their asymptotic properties they are so named as their relationship to the two stage least squares estimator 2sls is analogous to the relationship of the least absolute deviations estimator lad to the least squares estimator ls in the standard regression model the lad estimation has been extensively studied in the context of the standard regression model and its usefulness is universally recognized in this paper we show that the advantage of 2slad over 2sls in the simultaneous equations model can be as great as that of lad over ls in the standard regression model if 2slad is properly defined this last clause is very important since the results of this paper indicate that the lad analogue of 2sls that has been considered before in the literature is not an appropriate method

kernel estimation of hazard functions when observations have dependent and common covariates abstract we propose a hazard model where dependence between events is achieved by assuming dependence between covariates this model allows for correlated variables specific to observations as well as macro variables which all observations share this setup better fits many economic and financial applications where events are not independent nonparametric estimation of the hazard function is then studied kernel estimators proposed in nielsen and linton 1995 and linton et al 2003 are shown to have similar asymptotic properties compared with the i i d case mixing conditions ensure the asymptotic results follow these results depend on adjustments to bandwidth conditions simulations are conducted which verify the impact of dependence on estimators bandwidth selection accounting for dependence is shown to improve performance in an empirical application trade intensity in high frequency financial data is estimated

semiparametric estimation of a panel data proportional hazards model with fixed effects this paper considers a panel duration model that has a proportional hazards specification with fixed effects the paper shows how to estimate the baseline and integrated baseline hazard functions without assuming that they belong to known finite dimensional families of functions existing estimators assume that the baseline hazard function belongs to a known parametric family therefore the estimators presented here are more general than existing ones this paper also presents a method for estimating the parametric part of the proportional hazards model with dependent right censoring under which the partial likelihood estimator is inconsistent the paper presents some monte carlo evidence on the small sample performance of the new estimators

prediction and power transformations when the choice of power is restricted to a finite set abstract we study the family of power transformations proposed by box and cox 1964 when the choice of the power parameter is restricted to a finite set r the behavior of the box cox procedure is as anticipated in two extreme cases when the true parameter is an element of r and when is far from r we study the case in which 0 is close to r finding that the resulting methods can be very different from unrestricted maximum likelihood and that inferences may depend on the design the values of the regression parameters and the distance of to r

residual bounds for some or all singular values abstract for a given real or complex matrix we derive perturbation bounds for some or all singular values the bounds are of the same quality as corresponding residual bounds for the hermitian eigenvalue problem they are suitable for numerical computation

maximum likelihood estimation in semiparametric regression models with censored data summary semiparametric regression models play a central role in formulating the effects of covariates on potentially censored failure times and in the joint modelling of incomplete repeated measures and failure times in longitudinal studies the presence of infinite dimensional parameters poses considerable theoretical and computational challenges in the statistical analysis of such models we present several classes of semiparametric regression models which extend the existing models in important directions we construct appropriate likelihood functions involving both finite dimensional and infinite dimensional parameters the maximum likelihood estimators are consistent and asymptotically normal with efficient variances we develop simple and stable numerical techniques to implement the corresponding inference procedures extensive simulation experiments demonstrate that the inferential and computational methods proposed perform well in practical settings applications to three medical studies yield important new insights we conclude that there is no reason theoretical or numerical not to use maximum likelihood estimation for semiparametric regression models we discuss several areas that need further research

comparative study of coupling coefficients in dirichlet robin procedure for fluid structure aerothermal simulations this paper tests the performance of coupling coefficients of a dirichlet robin transmission procedure in the context of steady conjugate heat transfer cht particular emphasis is put on the optimal coefficients highlighted recently in a theoretical study based on a normal mode stability analysis this work can be seen as the logical continuation of that study in order to assess the relevance of the coefficients provided by the model problem in a realistic aerothermal computation first the numerical and physical cht modeling methodologies are presented then the optimal procedure applied to a dirichlet robin algorithm one coefficient method is briefly described in order to gauge the ability of this model to predict the stability and convergence properties of a realistic case it is compared on a heated cylinder in a flowfield test case a series of five coupling coefficients and three fourier numbers are considered these parameters are introduced into the model problem as data to compute the amplification factor and the stability limits the stability and convergence properties predicted by the model problem are then compared to those obtained in the cht computation this comparison shows an excellent overall agreement moreover for all the fourier numbers considered the numerical solution is stable and oscillation free when the optimal coefficient of the model problem is used this would suggest that the one dimensional normal mode analysis can provide relevant coefficients directly applicable to real cht problems

a note on jackknifing kernel regression function estimators corresp estimation of the value of a regression function at a point of continuity using a kernel type estimator is considered and improvements by a jackknife technique are discussed it is seen that a so called generalized jackknife estimator asymptotically improves upon an ordinary kernel type estimator however for a fixed sample size the generalized jackknife method may inflate the mean square error

an analysis of a new stable partitioned algorithm for fsi problems part i incompressible flow and elastic solids stable partitioned algorithms for fluid structure interaction fsi problems are developed and analyzed in this two part paper part i describes an algorithm for incompressible flow coupled with compressible elastic solids while part ii discusses an algorithm for incompressible flow coupled with structural shells importantly these new added mass partitioned amp schemes are stable and retain full accuracy with no sub iterations per time step even in the presence of strong added mass effects e g for light solids the numerical approach described here for bulk compressible solids extends the scheme of banks et al 1 2 for inviscid compressible flow and uses robin mixed boundary conditions with the fluid and solid solvers at the interface the basic amp robin conditions involving a linear combination of velocity and stress are determined from the outgoing solid characteristic relation normal to the fluid solid interface combined with the matching conditions on the velocity and traction two alternative forms of the amp conditions are then derived depending on whether the fluid equations are advanced with a fractional step method or not the stability and accuracy of the amp algorithm is evaluated for linearized fsi model problems the full nonlinear case being left for future consideration a normal mode analysis is performed to show that the new amp algorithm is stable for any ratio of the solid and fluid densities including the case of very light solids when added mass effects are large in contrast it is shown that a traditional partitioned algorithm involving a dirichlet neumann coupling for the same fsi problem is formally unconditionally unstable for any ratio of densities exact traveling wave solutions are derived for the fsi model problems and these solutions are used to verify the stability and accuracy of the corresponding numerical results obtained from the amp algorithm for the cases of light medium and heavy solids