homogenization limits and wigner transforms we present a theory for carrying out homogenization limits for quadratic functions called energy densities of solutions of initial value problems ivps with anti self adjoint spatial pseudo differential operators pdos the approach is based on the introduction of phase space wigner matrix measures that are calculated by solving kinetic equations involving the spectral properties of the pdo the weak limits of the energy densities are then obtained by taking moments of the wigner measure the very general theory is illustrated by typical examples like semi classical limits of schrodinger equations with or without a periodic potential the homogenization limit of the acoustic equation in a periodic medium and the classical limit of the dirac equation 1997 john wiley sons inc

analytical and bootstrap approximations to estimator distributions in l 1 regression abstract edgeworth and bootstrap approximations to estimator distributions in l1 regression are described analytic approximations based on edgeworth expansions that mix lattice and nonlattice components and allow for an intercept term in the regression are developed under mild conditions which do not even require a density for the error distribution under stronger assumptions on the error distribution the edgeworth expansion assumes a simpler form bootstrap approximations are described and the consistency of the bootstrap in the l 1 regression setting is established we show how the slow rate n 1 4 of convergence in this context of the standard unsmoothed bootstrap that resamples for the raw residuals may be improved to rate n 2 5 by two methods a smoothed bootstrap approach based on resampling from an appropriate kernel estimator of the error density and a normal approximation that uses a kernel estimator of the error density at a particular point its median 0 both of these methods require ch

simultaneous nonparametric inference of time series we consider kernel estimation of marginal densities and regression functions of stationary processes it is shown that for a wide class of time series with proper centering and scaling the maximum deviations of kernel density and regression estimates are asymptotically gumbel our results substantially generalize earlier ones which were obtained under independence or beta mixing assumptions the asymptotic results can be applied to assess patterns of marginal densities or regression functions via the construction of simultaneous confidence bands for which one can perform goodness of fit tests as an application we construct simultaneous confidence bands for drift and volatility functions in a dynamic short term rate model for the u s treasury yield curve rates data

multiscale phenomena green s functions the dirichlet to neumann formulation subgrid scale models bubbles and the origins of stabilized methods abstract an approach is developed for deriving variational methods capable of representing multiscale phenomena the ideas are first illustrated on the exterior problem for the helmholtz equation this leads to the well known dirichlet to neumann formulation next a class of subgrid scale models is developed and the relationships to bubble function methods and stabilized methods are established it is shown that both the latter methods are approximate subgrid scale models the identification for stabilized methods leads to an analytical formula for the intrinsic time scale whose origins have been a mystery heretofore

nonparametric estimates of regression quantiles and their local bahadur representation let x y be a random vector such that x is d dimensional y is real valued and y theta x varepsilon where x and varepsilon are independent and the alpha th quantile of varepsilon is 0 alpha is fixed such that 0 0 and set r p m 2p d where m is a nonnegative integer smaller than p let t theta denote a derivative of theta of order m it is proved that there exists a pointwise estimate hat t n of t theta based on a set of i i d observations x 1 y 1 cdots s n y n that achieves the optimal nonparametric rate of convergence n r under appropriate regularity conditions further a local bahadur type representation is shown to hold for the estimate hat t n and this is used to obtain some useful asymptotic results

inference on the quantile regression process tests based on the quantile regression process can be formulated like the classical kolmogorov smirnov and cramer von mises tests of goodness of fit employing the theory of bessel processes as in kiefer 1959 however it is frequently desirable to formulate hypotheses involving unknown nuisance parameters thereby jeopardizing the distribution free character of these tests we characterize this situation as the durbin problem since it was posed in durbin 1973 for parametric empirical processes in this paper we consider an approach to the durbin problem involving a martingale transformation of the parametric empirical process suggested by khmaladze 1981 and show that it can be adapted to a wide variety of inference problems involving the quantile regression process in particular we suggest new tests of the location shift and location scale shift models that underlie much of classical econometric inference the methods are illustrated with a reanalysis of data on unemployment durations from the pennsylvania reemployment bonus experiments the pennsylvania experiments conducted in 1988 89 were designed to test the efficacy of cash bonuses paid for early reemployment in shortening the duration of insured unemployment spells

construction of a probabilistic model for impedance matrices impedance matrices allow for the coupling of domains with very different properties and possibly modeled with different methods this paper presents the construction of a probabilistic model for such matrices using a nonparametric method that allows for the consideration of data errors as well as model errors to enable the application of this method in the case of a domain for which no finite element model is available the identification of a hidden state variables model from the knowledge of the impedance matrix at a discrete set of frequencies is also described finally the construction of the probabilistic model of the impedance of a pile foundation on a layered unbounded soil illustrates the capabilities of the method

on the bahadur representation of sample quantiles for dependent sequences we establish the bahadur representation of sample quantiles for linear and some widely used nonlinear processes local fluctuations of empirical processes are discussed applications to the trimmed and winsorized means are given our results extend previous ones by establishing sharper bounds under milder conditions and thus provide new insight into the theory of empirical processes for dependent random variables

rings whose cyclic modules are pure injective or pure projective abstract a famous theorem of algebra due to osofsky states that if every cyclic left r module is injective then r is semisimple therefore a natural question of this sort is what is the class of rings r for which every cyclic left r module is pure injective or pure projective the goal of this paper is to answer this question for instance we show that if every cyclic left r module is pure injective then r is a left perfect ring as a consequence a commutative coherent ring r is artinian if and only if every cyclic r module is pure injective also a commutative ring r is pure semisimple i e every r module is pure injective if and only if all cyclic r modules and all indecomposable r modules are pure injective we obtain some generalizations of osofsky s theorem in the cases r is semiprimitive or commutative coherent or a commutative semiprime goldie ring finally we show that a ring r is left noetherian if and only if every cyclic left r module is pure projective as a corollary of this result we obtain if every cyclic left r module is pure injective and pure projective then r is a left artinian ring the converse is also true when r is commutative

markov chain marginal bootstrap markov chain marginal bootstrap mcmb is a new method for constructing confidence intervals or regions for maximum likelihood estimators of certain parametric models and for a wide class of m estimators of linear regression the mcmb method distinguishes itself from the usual bootstrap methods in two important aspects it involves solving only one dimensional equations for parameters of any dimension and produces a markov chain rather than a conditionally independent sequence it is designed to alleviate computational burdens often associated with bootstrap in high dimensional problems the validity of mcmb is established through asymptotic analyses and illustrated with empirical and simulation studies for linear regression and generalized linear models

finite sample inference for quantile regression models abstract under minimal assumptions finite sample confidence bands for quantile regression models can be constructed these confidence bands are based on the conditional pivotal property of estimating equations that quantile regression methods solve and provide valid finite sample inference for linear and nonlinear quantile models with endogenous or exogenous covariates the confidence regions can be computed using markov chain monte carlo mcmc methods we illustrate the finite sample procedure through two empirical examples estimating a heterogeneous demand elasticity and estimating heterogeneous returns to schooling we find pronounced differences between asymptotic and finite sample confidence regions in cases where the usual asymptotics are suspect

the influence of uncertain local subsoil conditions on the response of buildings to ground vibration abstract this paper examines the influence of imperfect knowledge of the local subsoil conditions on the prediction of building response to ground borne vibration the focus is on problems of environmental ground vibration in the wide frequency range between 1 hz and 80 hz a probabilistic finite element perfectly matched layers model is developed for the analysis of the dynamic soil structure interaction problem where the shear modulus of the soil is modeled as a conditional random field a subdomain formulation is employed to impose loading by an incident wave field in the model the uncertainty on the subsoil properties is propagated to the response of a building by means of monte carlo simulation a case study is considered to investigate the influence of the spatial correlation length of the random field representing the shear modulus of the subsoil and the foundation type of the building the structural response uncertainty varies over frequency bands but as a general trend increases with frequency the foundation type of the building is a crucial parameter determining the structural response and the associated uncertainty bounds

censored regression quantiles abstract the object of this paper is to demonstrate how the lad estimation method for the censored regression model can be extended to more general quantiles in this paper the form of the conditional quantiles for the censored regression models is heuristically derived and discussed the resulting estimators of the regression coefficients which include the censored lad estimator as a special case are shown to be consistent and asymptotically normally distributed under appropriately translated versions of the corresponding assumptions for the former approach consistent estimation of the asymptotic covariance matrix when the error terms are i i d is also treated the paper discussed how several quantile estimators might be combined to improve efficiency when the error terms are identically distributed and how tests of homoskedasticity and symmetry of the error distribution can be constructed using particular differences of the estimated coefficients

regression rank scores and regression quantiles we show that regression quantiles which could be computed as solutions of a linear programming problem and the solutions of the corresponding dual problem which we call the regression rank scores generalize the duality of order statistics and of ranks from the location to the linear model noting this fact we study the regression quantile and regression rank score processes in the heteroscedastic linear regression model obtaining some new estimators and interesting comparisons with existing estimators

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

a direct approach to inference in nonparametric and semiparametric quantile models we construct a generic confidence interval for a conditional quantile via the direct approach it avoids estimating the conditional density function of the dependent variable given the covariate and is asymptotically valid for any conditional quantile any conditional quantile estimator and any data structure provided that certain weak convergence of the conditional quantile process holds for the original quantile estimator we also construct a generic confidence band for the conditional quantile function across a range of covariate values by using yang stute estimator and two semiparametric quantile functions we demonstrate the flexibility and simplicity of the direct approach the advantages of our new confidence intervals are borne out in a simulation study

a stochastic deterministic coupling method for continuum mechanics in this paper we present a novel approach that allows to couple a deterministic continuum model with a stochastic continuum one the coupling strategy is performed in the arlequin framework which is based on a volume coupling and a partition of the energy a suitable functional space is chosen for the weak enforcement of the continuity between the two models the choice of this space ensures that the mean of the stochastic solution equals the deterministic solution point wise and enforces appropriate boundary conditions on the stochastic dimension the proof of the existence of the solution of the mixed problem is provided the numerical strategy is also reviewed in particular with a view at the monte carlo method finally examples show the interest of the method and possible strategies for use in adaptive modeling

conditional empirical processes consider a sequence i xi yi i 1 of independent d 1 random vectors with common d f h text h left text x y right mathbb p left text x x y y right text left text x y right in mathbb r text d 1

efficient semiparametric estimation of quantile treatment effects this paper develops estimators for quantile treatment effects under the identifying restriction that selection to treatment is based on observable characteristics identification is achieved without requiring computation of the conditional quantiles of the potential outcomes instead the identification results for the marginal quantiles lead to an estimation procedure for the quantile treatment effect parameters that has two steps nonparametric estimation of the propensity score and computation of the difference between the solutions of two separate minimization problems root n consistency asymptotic normality and achievement of the semiparametric efficiency bound are shown for that estimator a consistent estimation procedure for the variance is also presented finally the method developed here is applied to evaluation of a job training program and to a monte carlo exercise results from the empirical application indicate that the method works relatively well even for a data set with limited overlap between treated and controls in the support of covariates the monte carlo study shows that for a relatively small sample size the method produces estimates with good precision and low bias especially for middle quantiles

characterization of the asymptotic distribution of semiparametric m estimators this paper develops a concrete formula for the asymptotic distribution of two step possibly non smooth semiparametric m estimators under general misspecification our regularity conditions are relatively straightforward to verify and also weaker than those available in the literature the first stage nonparametric estimation may depend on finite dimensional parameters we characterize 1 conditions under which the first stage estimation of nonparametric components do not affect the asymptotic distribution 2 conditions under which the asymptotic distribution is affected by the derivatives of the first stage nonparametric estimator with respect to the finite dimensional parameters and 3 conditions under which one can allow non smooth objective functions our framework is illustrated by applying it to three examples 1 profiled estimation of a single index quantile regression model 2 semiparametric least squares estimation under model misspecification and 3 a smoothed matching estimator 2010 elsevier b v all rights reserved

frequency content of randomly scattered signals the statistical properties of acoustic signals reflected by a randomly layered medium are analyzed when a pulsed spherical wave issuing from a point source is incident upon it the asymptotic analy

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

on local u statistic processes and the estimation of densities of functions of several sample variables a notion of local u statistic process is introduced and central limit theorems in various norms are obtained for it this involves the development of several inequalities for u processes that may be useful in other contexts this local u statistic process is based on an estimator of the density of a function of several sample variables proposed by frees j amer statist assoc 89 1994 517 525 and as a consequence uniform in bandwidth central limit theorems in the sup and in the lp norms are obtained for these estimators

conditional empirical likelihood estimation and inference for quantile regression models abstract this paper considers two empirical likelihood based estimation inference and specification testing methods for quantile regression models first we apply the method of conditional empirical likelihood cel by kitamura et al 2004 empirical likelihood based inference in conditional moment restriction models econometrica 72 1667 1714 and zhang and gijbels 2003 sieve empirical likelihood and extensions of the generalized least squares scandinavian journal of statistics 30 1 24 to quantile regression models second to avoid practical problems of the cel method induced by the discontinuity in parameters of cel we propose a smoothed counterpart of cel called smoothed conditional empirical likelihood scel we derive asymptotic properties of the cel and scel estimators parameter hypothesis tests and model specification tests important features are i the cel and scel estimators are asymptotically efficient and do not require preliminary weight estimation ii by inverting the cel and scel ratio parameter hypothesis tests asymptotically valid confidence intervals can be obtained without estimating the asymptotic variances of the estimators and iii in contrast to cel the scel method can be implemented by some standard newton type optimization simulation results demonstrate that the scel method in particular compares favorably with existing alternatives

rings in which every ideal is pure projective or fp projective abstract a ring r is said to be left pure hereditary resp rd hereditary if every left ideal of r is pure projective resp rd projective in this paper some properties and examples of these rings which are nontrivial generalizations of hereditary rings are given for instance we show that if r is a left rd hereditary left nonsingular ring then r is left noetherian if and only if u dim r r also we show that a ring r is quasi frobenius if and only if r is a left fgf left coherent right pure injective ring a ring r is said to be left fp hereditary if every left ideal of r is fp projective it is shown that if r is a left cf ring then r is left noetherian if and only if r is left pure hereditary if and only if r is left fp hereditary if and only if r is left coherent it is shown that every left self injective left fp hereditary ring is semiperfect finally it is shown that a ring r is left fp hereditary resp left coherent if and only if every submodule resp finitely generated submodule of a projective left r module is fp projective if and only if every pure factor module of an injective left r module is injective resp fp injective if and only if for each fp injective left r module u e u u is injective resp fp injective

rings over which every rd projective module is a direct sums of cyclically presented modules abstract we study direct sum decompositions of rd projective modules in particular we investigate the rings over which every rd projective right module is a direct sum of cyclically presented right modules or a direct sum of finitely presented cyclic right modules or a direct sum of right modules with local endomorphism rings ssp rings ssp rings are necessarily semiperfect for instance the superlocal rings introduced by puninski prest and rothmaler in 28 and the semilocal strongly regular rings introduced by kaplansky in 24 are ssp rings in the case of a noetherian ring r with further additional hypotheses an rd projective r module m turns out to be either a direct sum of finitely presented cyclic modules or of the form m t m p where t m is the torsion part of m elements of m annihilated by a regular element of r and p is a projective module

confidence intervals for the quantile of treatment effects in randomized experiments abstract in this paper we explore partial identification and inference for the quantile of treatment effects for randomized experiments first we propose nonparametric estimators of sharp bounds on the quantile of treatment effects and establish their asymptotic properties under general conditions second we construct confidence intervals for the bounds and the true quantile by using the approach in chernozhukov et al 2009 third under additional conditions we develop a new approach to construct confidence intervals for the bounds and the true quantile and refer to it as the order statistic approach a simulation study is conducted to investigate the finite sample performance of both approaches

nonparametric checks for single index models in this paper we study goodness of fit testing of single index models the large sample behavior of certain score type test statistics is investigated as a by product we obtain asymptotically distribution free maximin tests for a large class of local alternatives furthermore characteristic function based goodness of fit tests are proposed which are omnibus and able to detect peak alternatives simulation results indicate that the approximation through the limit distribution is acceptable already for moderate sample sizes applications to two real data sets are illustrated

linear functions of concomitants of order statistics with application to nonparametric estimation of a regression function abstract let xi yi i 1 2 n be independent identically distributed as x y then the rth ordered x variate is denoted by xr n and the associated y variate the concomitant of the rth order statistic by y r n this paper considers statistics of the form and more generally of the form where j is a bounded smooth function and may depend on n under certain regularity conditions the asymptotic normality of these statistics is established these statistics are used to construct consistent estimators of various conditional quantities for example e y x x p y a x x and var y x x

bootstrap methods for median regression models the least absolute deviations lad estimator for a median regression model does not satisfy the standard conditions for obtaining asymptotic refinements through use of the bootstrap because the lad objective function is not smooth this paper overcomes this problem by smoothing the objective function the smoothed estimator is asymptotically equivalent to the standard lad estimator with bootstrap critical values the rejection probabilities of symmetrical t and x 2 tests based on the smoothed estimator are correct through o n under the null hypothesis where 1 but can be arbitrarily close to 1 in contrast first order asymptotic approximations make errors of size o n these results also hold for symmetrical t and x 2 tests for censored median regression models

local linear quantile regression abstract in this article we study nonparametric regression quantile estimation by kernel weighted local linear fitting two such estimators are considered one is based on localizing the characterization of a regression quantile as the minimizer of e pp y a x x where p is the appropriate check function the other follows by inverting a local linear conditional distribution estimator and involves two smoothing parameters rather than one our aim is to present fully operational versions of both approaches and to show that each works quite well although either might be used in practice we have a particular preference for the second our automatic smoothing parameter selection method is novel the main regression quantile smoothing parameters are chosen by rule of thumb adaptations of state of the art methods for smoothing parameter selection for regression mean estimation the techniques are illustrated by application to two datasets and compared in simulations

direct use of regression quantiles to construct confidence sets in linear models direct use of the empirical quantile function provides a standard distribution free approach to constructing confidence intervals and confidence bands for population quantiles we apply this method to construct confidence intervals and confidence bands for regression quantiles and to construct prediction intervals based on sample regression quantiles comparison of the direct method with the studentization and the bootstrap methods are discussed simulation results show that the direct method has the advantage of robustness against departure from the normality assumption of the error terms

estimating the asymptotic covariance matrix for quantile regression models a monte carlo study abstract this monte carlo study examines several estimation procedures of the asymptotic covariance matrix in the quantile and censored quantile regression models design matrix bootstrap error bootstrapping order statistic sigma bootstrap homoskedastic kernel and heteroskedastic kernel the monte carlo samples are drawn from two alternative data sets 1 a the unaltered current population survey cps for 1987 and 2 b this cps data with independence between error term and regressors imposed this special setup allows one to evaluate the estimators under various realistic scenarios the results favor the design bootstrap for the general case but also support the order statistic when the error term is independent of the regressors

u processes rates of convergence on introduit un nouveau processus stochastique une collection de statistiques u indicees par une famille de noyaux symetriques on obtient des conditions pour la convergence presque sure uniforme d une suite de tels processus

quantile based nonparametric inference for first price auctions abstract we propose a quantile based nonparametric approach to inference on the probability density function pdf of the private values in first price sealed bid auctions with independent private values our method of inference is based on a fully nonparametric kernel based estimator of the quantiles and pdf of observable bids our estimator attains the optimal rate of guerre et al 2000 and is also asymptotically normal with an appropriate choice of the bandwidth

on average derivative quantile regression for fixed 0 1 the quantile regression function gives the th quantile x in the conditional distribution of a response variable y given the value x x of a vector of covariates it can be used to measure the effect of covariates not only in the center of a population but also in the upper and lower tails a functional that summarizes key features of the quantile specific relationship between x and y is the vector of weighted expected values of the vector of partial derivatives of the quantile function x in a nonparametric setting can be regarded as a vector of quantile specific nonparametric regression coefficients in survival analysis models e g cox s proportional hazard model proportional odds rate model accelerated failure time model and in monotone transformation models used in regression analysis gives the direction of the parameter vector in the parametric part of the model can also be used to estimate the direction of the parameter vector in semiparametric single index models popular in econometrics we show that under suitable regularity conditions the estimate of obtained by using the locally polynomial quantile estimate of chaudhuri 1991a is n 1 2 consistent and asymptotically normal with asymptotic variance equal to the variance of the influence function of the functional we discuss how the estimate of can be used for model diagnostics and in the construction of a link function estimate in general single index models

uniform in bandwidth consistency of kernel type function estimators we introduce a general method to prove uniform in bandwidth consistency of kernel type function estimators examples include the kernel density estimator the nadaraya watson regression estimator and the conditional empirical process our results may be useful to establish uniform consistency of data driven bandwidth kernel type function estimators

quantile and probability curves without crossing this paper proposes a method to address the longstanding problem of lack of monotonicity in estimation of conditional and structural quantile functions also known as the quantile crossing problem bassett and koenker 1982 the method consists in sorting or monotone rearranging the original estimated non monotone curve into a monotone rearranged curve we show that the rearranged curve is closer to the true quantile curve than the original curve in finite samples establish a functional delta method for rearrangement related operators and derive functional limit theory for the entire rearranged curve and its functionals we also establish validity of the bootstrap for estimating the limit law of the entire rearranged curve and its functionals our limit results are generic in that they apply to every estimator of a monotone function provided that the estimator satisfies a functional central limit theorem and the function satisfies some smoothness conditions consequently our results apply to estimation of other econometric functions with monotonicity restrictions such as demand production distribution and structural distribution functions we illustrate the results with an application to estimation of structural distribution and quantile functions using data on vietnam veteran status and earnings

identification in nonseparable models weak nonparametric restrictions are developed sufficient to identify the values of derivatives of structural functions in which latent random variables are nonseparable these derivatives can exhibit stochastic variation in a microeconometric context this allows the impact of a policy intervention as measured by the value of a structural derivative to vary across people who are identical as measured by covariates when the restrictions are satisfied quantiles of the distribution of a policy impact across people can be identified the identification restrictions are local in the sense that they are specific to the values of the covariates and the specific quantiles of latent variables at which identification is sought the conditions do not include the commonly required independence of latent variables and covariates they include local versions of the classical rank and order conditions and local quantile insensitivity conditions values of structural derivatives are identified by functionals of quantile regression functions and can be estimated using the same functionals applied to estimated quantile regression functions

when every finitely generated flat module is projective we investigate the class of rings over which every finitely generated flat right module is projective

identification of marginal effects in nonseparable models without monotonicity nonseparable models do not impose any type of additivity between the unobserved part and the observable regressors and are therefore ideal for many economic applications to identify these models using the entire joint distribution of the data as summarized in regression quantiles monotonicity in unobservables has frequently been assumed this paper establishes that in the absence of monotonicity the quantiles identify local average structural derivatives of nonseparable models

dynamics of structures coupled with elastic media a review of numerical models and methods abstract this paper reviews issues and developments in the field of structure environment interaction problems in which the environment is an elastic body possibly unbounded it covers in particular the fields of soil structure interaction ground borne noise and vibration emitted by transportation systems and wave diffraction by obstacles in an elastic medium the general setting for a linear bounded structure coupled to an unbounded linear environment through a bounded interface is first recalled and the domain decomposition technique classically used for its description is put up extensions for the cases of nonlinear structure and uncertain environment are then discussed finally the ongoing research in the fields of unbounded interface moving interface and multiple interfaces are reviewed and summarized

non crossing non parametric estimates of quantile curves in this paper a new nonparametric estimate of conditional quantiles is proposed that avoids the problem of crossing quantile curves calculated for various p 2 0 1 the method uses an initial estimate of the conditional distribution function in a flrst step and solves the problem of inversion and monotonization with respect to p 2 0 1 simultaneously it is demonstrated that the new estimates are asymptotically normal distributed and asymptotically flrst order equivalent to quantile estimates obtained by local constant or local linear smoothing of the conditional distribution function the performance of the new procedure is illustrated by means of a simulation study and some comparisons with the currently available procedures which are similar in spirit with the proposed method are presented

an alternative estimator for the censored quantile regression model the paper introduces an alternative estimator for the linear censored quantile regression model the objective function is globally convex and the estimator is a solution to a linear programming problem hence a global minimizer is obtained in a finite number of simplex iterations the suggested estimator also applies to the case where the censoring point is an unknown function of a set of regressors it is shown that under fairly weak conditions the estimator has a n convergence rate and is asymptotically normal in the case of a fixed censoring point its asymptotic property is nearly equivalent to that of the estimator suggested by powell 1984 1986a a monte carlo study performed shows that the suggested estimator has very desirable small sample properties it precisely corrects for the bias induced by censoring even when there is a large amount of censoring and for relatively small sample sizes

methods for estimating a conditional distribution function abstract motivated by the problem of setting prediction intervals in time series analysis we suggest two new methods for conditional distribution estimation the first method is based on locally fitting a logistic model and is in the spirit of recent work on locally parametric techniques in density estimation it produces distribution estimators that may be of arbitrarily high order but nevertheless always lie between 0 and 1 the second method involves an adjusted form of the nadaraya watson estimator it preserves the bias and variance properties of a class of second order estimators introduced by yu and jones but has the added advantage of always being a distribution itself our methods also have application outside the time series setting for example to quantile estimation for independent data this problem motivated the work of yu and jones

bivariate quantile smoothing splines it has long been recognized that the mean provides an inadequate summary whereas the set of quantiles can supply a more complete description of a sample we introduce bivariate quantile smoothing splines which belong to the space of bilinear tensor product splines as nonparametric estimators for the conditional quantile functions in a two dimensional design space the estimators can be computed by using standard linear programming techniques and can further be used as building blocks for conditional quantile estimations in higher dimensions for moderately large data sets we recommend penalized bivariate b splines as approximate solutions we use real and simulated data to illustrate the methodology proposed

elastic wave propagation in a 3 d unbounded random heterogeneous medium coupled with a bounded medium application to seismic soil structure interaction sssi we present a sub structuring method for the coupling between a large elastic structure and a stratified soil half space exhibiting random heterogeneities over a bounded domain and impinged by incident waves both media are also weakly dissipative the concept of interfaces classically used in sub structuring methods is extended to volume interfaces in the proposed approach the random dimension of the stochastic fields modelling the heterogeneities in the soil is reduced by introducing a karhunen loeve expansion of these stochastic fields the coupled overall problem is solved by monte carlo simulation techniques a realistic example of a large industrial structure interacting with an uncertain stratified soil medium under earthquake is finally presented this case study and others validate the presented methodology and its ability to handle complex mechanical systems copyright 2002 john wiley sons ltd

kernel and nearest neighbor estimation of a conditional quantile let x 1 z 1 x 2 z 2 x n z n be iid as x z and for 0 p 1 let p x denote the conditional p quantile of z given x x i e p z p x x x p kernel and near est neighbor estimators of p x are proposed in order to study the asymptotics of these estimates bahadur type representations of the sample conditional quantiles are obtained these representations are used to examine the important issue of choosing the smoothing parameter by a local approach for a fixed x based on weak convergence of these estimators the results lead to asymptotic linear models which motivate certain estimators