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

quantile regression adjusting for dependent censoring from semicompeting risks in this work we study quantile regression when the response is an event time subject to potentially dependent censoring we consider the semi competing risks setting where time to censoring remains observable after the occurrence of the event of interest while such a scenario frequently arises in biomedical studies most of current quantile regression methods for censored data are not applicable because they generally require the censoring time and the event time be independent by imposing rather mild assumptions on the association structure between the time to event response and the censoring time variable we propose quantile regression procedures which allow us to garner a comprehensive view of the covariate effects on the event time outcome as well as to examine the informativeness of censoring an efficient and stable algorithm is provided for implementing the new method we establish the asymptotic properties of the resulting estimators including uniform consistency and weak convergence the theoretical development may serve as a useful template for addressing estimating settings that involve stochastic integrals extensive simulation studies suggest that the proposed method performs well with moderate sample sizes we illustrate the practical utility of our proposals through an application to a bone marrow transplant trial

instrumental variable quantile regression a robust inference approach abstract in this paper we develop robust inference procedures for an instrumental variables model defined by y d u where d u is strictly increasing in u and u is a uniform variable that may depend on d but is independent of a set of instrumental variables z the proposed inferential procedures are computationally convenient in typical applications and can be carried out using software available for ordinary quantile regression our inferential procedure arises naturally from an estimation algorithm and has the important feature of being robust to weak and partial identification and remains valid even in cases where identification fails completely the use of the proposed procedures is illustrated through two empirical examples

simple resampling methods for censored regression quantiles abstract powell journal of econometrics 25 1984 303 325 journal of econometrics 32 1986 143 155 considered censored regression quantile estimators the asymptotic covariance matrices of his estimators depend on the error densities and are therefore difficult to estimate reliably the difficulty may be avoided by applying the bootstrap method hahn econometric theory 11 1995 105 121 calculation of the estimators however requires solving a nonsmooth and nonconvex minimization problem resulting in high computational costs in implementing the bootstrap we propose in this paper computationally simple resampling methods by convexfying powell s approach in the resampling stage a major advantage of the new methods is that they can be implemented by efficient linear programming simulation studies show that the methods are reliable even with moderate sample sizes

lyapunov theory for 2 d nonlinear roesser models application to asymptotic and exponential stability this technical note deals with a general class of discrete 2 d possibly nonlinear systems based on the roesser model we first motivate the introduction of lyapunov type definitions of asymptotic and exponential stability this will allow us to introduce and discuss several particularities that cannot be found in 1 d systems once this background has been carefully designed we develop different lyapunov theorems in order to check asymptotic and exponential stability of nonlinear 2 d systems finally we propose the first converse lyapunov theorem in the case of exponential stability

censored quantile regression with partially functional effects quantile regression offers a flexible approach to analyzing survival data allowing each covariate effect to vary with quantiles in practice constancy is often found to be adequate for some covariates in this paper we study censored quantile regression tailored to the partially functional effect setting with a mixture of varying and constant effects such a model can offer a simpler view regarding covariate survival association and moreover can enable improvement in estimation efficiency we propose profile estimating equations and present an iterative algorithm that can be readily and stably implemented asymptotic properties of the resultant estimators are established a simple resampling based inference procedure is developed and justified extensive simulation studies demonstrate efficiency gains of the proposed method over a naive two stage procedure the proposed method is illustrated via an application to a recent renal dialysis study

an adapted loss function for censored quantile regression in this article we study a novel approach for the estimation of quantiles when facing potential right censoring of the responses contrary to the existing literature on the subject the adopted st

quantile regression for duration models with time varying regressors abstract since the seminal work of koenker and bassett 1978 quantile regression has become a widely used tool in duration analysis the existing literature however has focused on time invariant regressors even though time varying regressors are common in practice in this paper we introduce a quantile regression framework with time varying regressors and develop quantile regression estimators our estimators are motivated by manski s 1975 1985 maximum score estimator and chen s 2010 integrated maximum score estimator our estimators are consistent and asymptotically normal under some regularity conditions and perform well in finite samples our method is illustrated with an unemployment duration data set

exact computation of censored least absolute deviations estimator abstract we show that exact computation of the censored least absolute deviations clad estimator proposed by powell 1984 may be achieved by formulating the estimator as a linear mixed integer programming mip problem with disjunctive constraints we apply our approach to three previously studied datasets and find that widely used approximate optimization algorithms can lead to erroneous conclusions extensive simulations confirm that mip based computation using available solvers is effective for datasets typically encountered in econometric applications and that despite the proliferation of competitors clad remains a useful estimator

bayesian quantile regression for censored data ummary in this paper we propose a semiparametric quantile regression model for censored survival data quantile regression permits covariates to affect survival differently at different stages in the follow up period thus providing a comprehensive study of the survival distribution we take a semiparametric approach representing the quantile process as a linear combination of basis functions the basis functions are chosen so that the prior for the quantile process is centered on a simple location scale model but flexible enough to accommodate a wide range of quantile processes we show in a simulation study that this approach is competitive with existing methods the method is illustrated using data from a drug treatment study where we find that the bayesian model often gives smaller measures of uncertainty than its competitors and thus identifies more significant effects

generalizing quantile regression for counting processes with applications to recurrent events in survival analysis quantile regression has become a useful approach to account for covariate effects on the distribution of an event time of interest in this article we discuss how quantile regression can be extended to model counting processes and thus lead to a broader regression framework for survival data we specifically investigate the proposed modeling of counting processes for recurrent events data we show that the new recurrent events model retains the desirable features of quantile regression such as easy interpretation and good model flexibility while accommodating various observation schemes encountered in observational studies we develop a general theoretical and inferential framework for the new counting process model which unifies with an existing method for censored quantile regression as another useful contribution of this work we propose a sample based covariance estimation procedure which provides a useful complement to the prevailing bootstrapping approach we demonstrate t

median regression models for longitudinal data with dropouts summary recently median regression models have received increasing attention when continuous responses follow a distribution that is quite different from a normal distribution usual mean regression models may fail to produce efficient estimators whereas median regression models may perform satisfactorily in this article we discuss using median regression models to deal with longitudinal data with dropouts weighted estimating equations are proposed to estimate the median regression parameters for incomplete longitudinal data where the weights are determined by modeling the dropout process consistency and the asymptotic distribution of the resultant estimators are established the proposed method is used to analyze a longitudinal data set arising from a controlled trial of hiv disease volberding et al 1990 the new england journal of medicine 322 941 949 simulation studies are conducted to assess the performance of the proposed method under various situations an extension to estimation of the association parameters is outlined

inference on endogenously censored regression models using conditional moment inequalities under a quantile restriction randomly censored regression models can be written in terms of conditional moment inequalities we study the identified features of these moment inequalities with respect to the regression parameters where we allow for covariate dependent censoring endogenous censoring and endogenous regressors these inequalities restrict the parameters to a set we show regular point identification can be achieved under a set of interpretable sufficient conditions we then provide a simple way to convert conditional moment inequalities into unconditional ones while preserving the informational content our method obviates the need for nonparametric estimation which would require the selection of smoothing parameters and trimming procedures without the point identification conditions our objective function can be used to do inference on the partially identified parameter maintaining the point identification conditions we propose a quantile minimum distance estimator which converges at the parametric rate to the parameter vector of interest and has an asymptotically normal distribution a small scale simulation study and an application using drug relapse data demonstrate satisfactory finite sample performance

sequential estimation of censored quantile regression models abstract in this paper we propose sequential censored quantile regression scqr and sequential instrumental variables censored quantile regression estimators sivcqr we effectively transform the difficult censored quantile regression and censored instrumental variables quantile regression problems into more standard qr and ivqr procedures consequently our approaches make the quantile regression techniques for censored data easily accessible to applied researchers simulation results show that both estimators perform well

transformed dynamic quantile regression on censored data we propose a class of power transformed linear quantile regression models for time to event observations subject to censoring by introducing a process of power transformation with different transf

partial identification and inference in censored quantile regression abstract in this paper we study partial identification and inference in a linear quantile regression where the dependent variable is subject to possibly unknown dependent censoring characterized by an archimedean copula an outer set of the identified set for the regression coefficient is characterized via inequality constraints for one parameter ordered families of archimedean copulas we construct a simple confidence set by inverting an asymptotically pivotal statistic a bootstrap confidence set is also constructed sensitivity of the identified set to possible misspecification of the true copula and the finite sample performance of the boostrap confidence set are investigated numerically

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

quantile association for bivariate survival data bivariate survival data arise frequently in familial association studies of chronic disease onset as well as in clinical trials and observational studies with multiple time to event endpoints the association between two event times is often scientifically important in this article we examine the association via a novel quantile association measure which describes the dynamic association as a function of the quantile levels the quantile association measure is free of marginal distributions allowing direct evaluation of the underlying association pattern at different locations of the event times we propose a nonparametric estimator for quantile association as well as a semiparametric estimator that is superior in smoothness and efficiency the proposed methods possess desirable asymptotic properties including uniform consistency and root n convergence they demonstrate satisfactory numerical performances under a range of dependence structures an application of our methods suggests interesting association patterns between time to myocardial infarction and time to stroke in an atherosclerosis study

partial rank estimation of duration models with general forms of censoring abstract in this paper we propose estimators for the regression coefficients in censored duration models which are distribution free impose no parametric specification on the baseline hazard function and can accommodate general forms of censoring the estimators are shown to have desirable asymptotic properties and monte carlo simulations demonstrate good finite sample performance among the data features the new estimators can accommodate are covariate dependent censoring double censoring and fixed individual or group specific effects we also examine the behavior of the estimator in an empirical illustration

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

quantile regression with measurement error regression quantiles can be substantially biased when the covariates are measured with error in this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error the method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels an iterative em type estimation algorithm to obtain the solutions to such joint estimation equations is provided the finite sample performance of the proposed method is investigated in a simulation study and compared to the standard regression calibration approach finally we apply our methodology to part of the national collaborative perinatal project growth data a longitudinal study with an unusual measurement error structure

inference for censored quantile regression models in longitudinal studies we develop inference procedures for longitudinal data where some of the measurements are censored by fixed constants we consider a semi parametric quantile regression model that makes no distributional assumptions our research is motivated by the lack of proper inference procedures for data from biomedical studies where measurements are censored due to a fixed quantification limit in such studies the focus is often on testing hypotheses about treatment equality to this end we propose a rank score test for large sample inference on a subset of the covariates we demonstrate the importance of accounting for both censoring and intra subject dependency and evaluate the performance of our proposed methodology in a simulation study we then apply the proposed inference procedures to data from an aids related clinical trial we conclude that our framework and proposed methodology is very valuable for differentiating the influences of predictors at different locations in the conditional distribution of a response variable

smoothed and corrected score approach to censored quantile regression with measurement errors censored quantile regression is an important alternative to the cox proportional hazards model in survival analysis in contrast to the usual central covariate effects quantile regression can effectively characterize the covariate effects at different quantiles of the survival time when covariates are measured with errors it is known that naively treating mismeasured covariates as error free would result in estimation bias under censored quantile regression we propose smoothed and corrected estimating equations to obtain consistent estimators we establish consistency and asymptotic normality for the proposed estimators of quantile regression coefficients compared with the naive estimator the proposed method can eliminate the estimation bias under various measurement error distributions and model error distributions we conduct simulation studies to examine the finite sample properties of the new method and apply it to a lung cancer study supplementary materials for this article are available online

quantile regression with censoring and endogeneity in this paper we develop a new censored quantile instrumental variable cqiv estimator and describe its properties and computation the cqiv estimator combines powell 1986 censored quantile regression cqr to deal with censoring with a control variable approach to incorporate endogenous regressors the cqiv estimator is obtained in two stages that are nonadditive in the unobservables the first stage estimates a nonadditive model with infinite dimensional parameters for the control variable such as a quantile or distribution regression model the second stage estimates a nonadditive censored quantile regression model for the response variable of interest including the estimated control variable to deal with endogeneity for computation we extend the algorithm for cqr developed by chernozhukov and hong 2002 to incorporate the estimation of the control variable we give generic regularity conditions for asymptotic normality of the cqiv estimator and for the validity of resampling methods to approximate its asymptotic distribution we verify these conditions for quantile and distribution regression estimation of the control variable our analysis covers two stage uncensored quantile regression with nonadditive first stage as an important special case we illustrate the computation and applicability of the cqiv estimator with a monte carlo numerical example and an empirical application on estimation of engel curves for alcohol

further improvements in the calculation of censored quantile regressions censored quantile regressions of powell 1984 1986 are very powerful inferencing tools in economics and engineering as the calculation of censored quantile regressions involves minimizing a nonconvex and nondifferentiable function global optimization techniques can be the only breakthroughs the first implementation of a global optimization technique namely threshold accepting of fitzenberger and winker 1998 2007 is challenged by the genetic algorithm ga in this paper the results show that the ga provides substantial improvements over threshold accepting for cases with randomly distributed censoring points

estimation and inference of quantile regression for survival data under biased sampling abstractbiased sampling occurs frequently in economics epidemiology and medical studies either by design or due to data collecting mechanism failing to take into account the sampling bias usually leads to incorrect inference we propose a unified estimation procedure and a computationally fast resampling method to make statistical inference for quantile regression with survival data under general biased sampling schemes including but not limited to the length biased sampling the case cohort design and variants thereof we establish the uniform consistency and weak convergence of the proposed estimator as a process of the quantile level we also investigate more efficient estimation using the generalized method of moments and derive the asymptotic normality we further propose a new resampling method for inference which differs from alternative procedures in that it does not require to repeatedly solve estimating equations it is proved that the resampling method consistently estimates the asymptoti

censored quantile regression survival models with a cure proportion abstract a new quantile regression model for survival data is proposed that permits a positive proportion of subjects to become unsusceptible to recurrence of disease following treatment or based on other observable characteristics in contrast to prior proposals for quantile regression estimation of censored survival models we propose a new data augmentation approach to estimation our approach has computational advantages over earlier approaches proposed by wu and yin 2013 2017 we compare our method with the two estimation strategies proposed by wu and yin and demonstrate its advantageous empirical performance in simulations the methods are also illustrated with data from a lung cancer survival study

semiparametric robust estimation of truncated and censored regression models many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares scls are sensitive to outliers and data contamination as we document therefore we propose a semiparametric general trimmed estimator gte of truncated and censored regression which is highly robust but relatively imprecise to improve its performance we also propose data adaptive and one step trimmed estimators we derive the robust and asymptotic properties of all proposed estimators and show that the one step estimators e g one step scls are as robust as gte and are asymptotically equivalent to the original estimator e g scls the finite sample properties of existing and proposed estimators are studied by means of monte carlo simulations

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

estimation of censored quantile regression for panel data with fixed effects this article investigates estimation of censored quantile regression qr models with fixed effects standard available methods are not appropriate for estimation of a censored qr model with a large number of parameters or with covariates correlated with unobserved individual heterogeneity motivated by these limitations the article proposes estimators that are obtained by applying fixed effects qr to subsets of observations selected either parametrically or nonparametrically we derive the limiting distribution of the new estimators under joint limits and conduct monte carlo simulations to assess their small sample performance an empirical application of the method to study the impact of the 1964 civil rights act on the black white earnings gap is considered supplementary materials for this article are available online

functional censored quantile regression we propose a functional censored quantile regression model to describe the time varying relationship between time to event outcomes and corresponding functional covariates the time varying effect

survival analysis with quantile regression models quantile regression offers great flexibility in assessing covariate effects on event times thereby attracting considerable interests in its applications in survival analysis but currently available methods often require stringent assumptions or complex algorithms in this article we develop a new quantile regression approach for survival data subject to conditionally independent censoring the proposed martingale based estimating equations naturally lead to a simple algorithm that involves minimizations only of l1 type convex functions we establish uniform consistency and weak convergence of the resultant estimators we develop inferences accordingly including hypothesis testing second stage inference and model diagnostics we evaluate the finite sample performance of the proposed methods through extensive simulation studies an analysis of a recent dialysis study illustrates the practical utility of our proposals

conditional quantile screening in ultrahigh dimensional heterogeneous data summary to accommodate the heterogeneity that is often present in ultrahigh dimensional data we propose a conditional quantile screening method which enables us to select features that contribute to the conditional quantile of the response given the covariates the method can naturally handle censored data by incorporating a weighting scheme through redistribution of the mass to the right moreover it is invariant to monotone transformation of the response and requires substantially weaker conditions than do alternative methods we establish sure independent screening properties for both the complete and the censored response cases we also conduct simulations to evaluate the finite sample performance of the proposed method and compare it with existing approaches

correction to censored regression quantiles by s portnoy 98 2003 1001 1012 2006 correction to censored regression quantiles by s portnoy 98 2003 1001 1012 journal of the american statistical association vol 101 no 474 pp 860 861

quantile regression for doubly censored data double censoring often occurs in registry studies when left censoring is present in addition to right censoring in this work we propose a new analysis strategy for such doubly censored data by adopting a quantile regression model we develop computationally simple estimation and inference procedures by appropriately using the embedded martingale structure asymptotic properties including the uniform consistency and weak convergence are established for the resulting estimators moreover we propose conditional inference to address the special identifiability issues attached to the double censoring setting we further show that the proposed method can be readily adapted to handle left truncation simulation studies demonstrate good finite sample performance of the new inferential procedures the practical utility of our method is illustrated by an analysis of the onset of the most commonly investigated respiratory infection pseudomonas aeruginosa in children with cystic fibrosis through the use of the u s cystic fibrosis registry

quantile calculus and censored regression quantile regression has been advocated in survival analysis to assess evolving covariate effects however challenges arise when the censoring time is not always observed and may be covariate dependent particularly in the presence of continuously distributed covariates in spite of several recent advances existing methods either involve algorithmic complications or impose a probability grid the former leads to difficulties in the implementation and asymptotics whereas the latter introduces undesirable grid dependence to resolve these issues we develop fundamental and general quantile calculus on cumulative probability scale in this article upon recognizing that probability and time scales do not always have a one to one mapping given a survival distribution these results give rise to a novel estimation procedure for censored quantile regression based on estimating integral equations a numerically reliable and efficient progressive localized minimization plmin algorithm is proposed for the computation this procedure reduces exactly to the kaplan meier method in the k sample problem and to standard uncensored quantile regression in the absence of censoring under regularity conditions the proposed quantile coefficient estimator is uniformly consistent and converges weakly to a gaussian process simulations show good statistical and algorithmic performance the proposal is illustrated in the application to a clinical study

censored median regression using weighted empirical survival and hazard functions abstract for median regression models that regress the median of the survival time or a transform thereof on the covariates some semi parametric estimators that include the intercept component are introduced when the survival time may be censored these new median regression estimators do not require estimating the censoring distributions they can be viewed as an extension of the sample median to the censored regression model these estimators are based on some weighted empirical survival and hazard functions and are shown to be consistent and asymptotically normal they performed very well in various numerical studies the proposed procedures are illustrated in some real data examples

estimation of high conditional quantiles for heavy tailed distributions estimation of conditional quantiles at very high or low tails is of interest in numerous applications quantile regression provides a convenient and natural way of quantifying the impact of covariates at different quantiles of a response distribution however high tails are often associated with data sparsity so quantile regression estimation can suffer from high variability at tails especially for heavy tailed distributions in this article we develop new estimation methods for high conditional quantiles by first estimating the intermediate conditional quantiles in a conventional quantile regression framework and then extrapolating these estimates to the high tails based on reasonable assumptions on tail behaviors we establish the asymptotic properties of the proposed estimators and demonstrate through simulation studies that the proposed methods enjoy higher accuracy than the conventional quantile regression estimates in a real application involving statistical downscaling of daily precipitation in

competing risks quantile regression quantile regression has emerged as a significant extension of traditional linear models and its potential in survival applications has recently been recognized in this paper we study quantile regression with competing risks data formulating the model based on conditional quantiles defined using the cumulative incidence function which includes as a special case an analog to the usual accelerated failure time model the proposed competing risks quantile regression model provides meaningful physical interpretations of covariate effects and moreover relaxes the constancy constraint on regression coefficients thereby providing a useful perhaps more flexible alternative to the popular subdistribution proportional hazards model we derive an unbiased monotone estimating equation for regression parameters in the quantile model the uniform consistency and weak convergence of the resulting estimators are established across a quantile continuum we develop inferences including covariance estimation second

multiple imputation for m regression with censored covariates we develop a new multiple imputation approach for m regression models with censored covariates instead of specifying parametric likelihoods our method imputes the censored covariates by their conditional quantiles given the observed data where the conditional quantiles are estimated through fitting a censored quantile regression process the resulting estimator is shown to be consistent and asymptotically normal and it improves the estimation efficiency by using information from cases with censored covariates compared with existing methods the proposed method is more flexible as it does not require stringent parametric assumptions on the distributions of either the regression errors or the covariates the finite sample performance of the proposed method is assessed through a simulation study and the analysis of a c reactive protein dataset in the 2007 2008 national health and nutrition examination survey this article has supplementary material online

on multivariate quantiles under partial orders this paper focuses on generalizing quantiles from the ordering point of view we propose the concept of partial quantiles which are based on a given partial order we establish that partial quantiles are equivariant under order preserving transformations of the data robust to outliers characterize the probability distribution if the partial order is sufficiently rich generalize the concept of efficient frontier and can measure dispersion from the partial order perspective we also study several statistical aspects of partial quantiles we provide estimators associated rates of convergence and asymptotic distributions that hold uniformly over a continuum of quantile indices furthermore we provide procedures that can restore monotonicity properties that might have been disturbed by estimation error establish computational complexity bounds and point out a concentration of measure phenomenon the latter under independence and the componentwise natural order finally we illustrate the concepts by discussing several theoretical examples and simulations empirical applications to compare intake nutrients within diets to evaluate the performance of investment funds and to study the impact of policies on tobacco awareness are also presented to illustrate the concepts and their use

quantile regression models with multivariate failure time data en tant qu alternative a la classique modelisation de la moyenne par regression la modelisation des quantiles a ete largement etudiee surtout celle de la mediane dans le cas de donnees de survie independantes cependant les correlations naturelles ou artificielles entre donnees font qu il est courant de rencontrer en recherche biomedicale des donnees de survie multivariees pour lesquelles une correlation intra groupe doit etre ajustee pour des donnees de survie correlees et censurees a droite nous explorons ici la modelisation des quantiles et adaptons une approche par equations d estimation destinee a estimer les parametres sous hypothese d independance nous adaptons egalement une version ponderee de cette meme approche qui en ameliore l efficacite nous montrons que les estimateurs des parametres sont robustes et suivent asymptotiquement une distribution normale l estimation de la variance obtenue par approximation asymptotique utilise une estimation non parametrique de la densite cependant que la matrice de variance covariance est estimee par bootstrap et methodes de re echantillonnage perturbe des simulations montrent que l approche proposee convient aux echantillons de taille finie cette nouvelle demarche est appliquee pour illustration a des donnees issues d un essai clinique dans l otite

multiple imputation for cure rate quantile regression with censored data summary the main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured or whether they are susceptible or insusceptible to the event of interest considering the susceptible indicator as missing data we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction we develop an iterative algorithm to estimate the conditionally uncured probability for each subject by utilizing this estimated probability and bernoulli sample imputation we can classify each subject as cured or uncured and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects repeating the imputation procedure multiple times and taking an average over the resultant estimators we obtain consistent estimators for the quantile regression coefficients our approach relaxes the usual global linearity assumption so that we can apply quantile regression to any particular quantile of interest we establish asymptotic properties for the proposed estimators including both consistency and asymptotic normality we conduct simulation studies to assess the finite sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration

quantile adaptive model free variable screening for high dimensional heterogeneous data we introduce a quantile adaptive framework for nonlinear variable screening with high dimensional heterogeneous data this framework has two distinctive features 1 it allows the set of active variables to vary across quantiles thus making it more flexible to accommodate heterogeneity 2 it is model free and avoids the difficult task of specifying the form of a statistical model in a high dimensional space our nonlinear independence screening procedure employs spline approximations to model the marginal effects at a quantile level of interest under appropriate conditions on the quantile functions without requiring the existence of any moments the new procedure is shown to enjoy the sure screening property in ultra high dimensions furthermore the quantile adaptive framework can naturally handle censored data arising in survival analysis we prove that the sure screening property remains valid when the response variable is subject to random right censoring numerical studies confirm the fine performance of the proposed method for various semiparametric models and its effectiveness to extract quantile specific information from heteroscedastic data

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

locally weighted censored quantile regression censored quantile regression offers a valuable supplement to cox proportional hazards model for survival analysis existing work in the literature often requires stringent assumptions such as unconditional independence of the survival time and the censoring variable or global linearity at all quantile levels moreover some of the work uses recursive algorithms making it challenging to derive asymptotic normality to overcome these drawbacks we propose a new locally weighted censored quantile regression approach that adopts the redistribution of mass idea and employs a local reweighting scheme its validity only requires conditional independence of the survival time and the censoring variable given the covariates and linearity at the particular quantile level of interest our method leads to a simple algorithm that can be conveniently implemented with r software applying recent theory of m estimation with infinite dimensional parameters we establish the consistency and asymptotic normality of the pr

a statistical mechanics approach to the inherent states of granular media abstract we consider a statistical mechanics approach to granular systems by following the original ideas developed by edwards we use the concept of inherent states defined as the stable configurations in the potential energy landscape introduced in the context of glasses under simplifying assumptions the equilibrium inherent states can be characterized by a configurational temperature 1 we link to edwards compactivity and address the problem of its experimental measure we also discuss the possibility to describe the time dependent distribution probability in the inherent states with an appropriate master equation

set identification of the censored quantile regression model for short panels with fixed effects this paper studies identification and estimation of a censored quantile regression model for short panel data with fixed effects using the redistribution of mass idea we obtain bounds on the conditional distribution of differences of the model across periods under conditional quantile restrictions together with a weak conditional independence assumption along the lines of rosen 2012 the inversion of the distribution bounds characterizes the sharp identified set via a set of inequalities based on conditional quantile functions due to the presence of censoring some of the inequalities defining the identified set hold trivially and have no identification power moreover those trivial inequalities cause a difficulty in estimating the identified set to deal with the issue we propose a two step estimation method where the first step consists of excluding trivial inequalities and the second step performs minimization of a convex criterion function using the remaining inequalities we establish asymptotic properties of the set estimator and also consider sufficient conditions under which point identification can be attained

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

three step censored quantile regression and extramarital affairs this article suggests very simple three step estimators for censored quantile regression models with a separation restriction on the censoring probability the estimators are theoretically attractive i e asymptotically as efficient as the celebrated powell s censored least absolute deviation estimator at the same time they are conceptually simple and have trivial computational expenses they are especially useful in samples of small size or models with many regressors with desirable finite sample properties and small bias the separation restriction costs a small reduction of generality relative to the canonical censored regression quantile model yet its main plausible features remain intact the estimator can also be used to estimate a large class of traditional models including the normal amemiya tobin model and many accelerated failure and proportional hazard models we illustrate the approach with an extramarital affairs example and contrast our findings with those of fair

survival analysis with median regression models abstract the median is a simple and meaningful measure for the center of a long tailed survival distribution to examine the covariate effects on survival a natural alternative to the usual mean regression model is to regress the median of the failure time variable or a transformation thereof on the covariates in this article we propose semiparametric procedures to make inferences for such median regression models with possibly censored observations our proposals can be implemented efficiently using a simulated annealing algorithm numerical studies are conducted to show the advantage of the new procedures over some recently developed methods for the accelerated failure time model a special type of mean regression models in the survival analysis the proposals discussed in the article are illustrated with a lung cancer data set

joint estimation of quantile planes over arbitrary predictor spaces abstractin spite of the recent surge of interest in quantile regression joint estimation of linear quantile planes remains a great challenge in statistics and econometrics we propose a novel parameterization that characterizes any collection of noncrossing quantile planes over arbitrarily shaped convex predictor domains in any dimension by means of unconstrained scalar vector and function valued parameters statistical models based on this parameterization inherit a fast computation of the likelihood function enabling penalized likelihood or bayesian approaches to model fitting we introduce a complete bayesian methodology by using gaussian process prior distributions on the function valued parameters and develop a robust and efficient markov chain monte carlo parameter estimation the resulting method is shown to offer posterior consistency under mild tail and regularity conditions we present several illustrative examples where the new method is compared against existing approaches and is found to o

mixtures of polya trees for flexible spatial frailty survival modelling mixtures of polya trees offer a very flexible nonparametric approach for modelling time to event data many such settings also feature spatial association that requires further sophistication either at the point level or at the lattice level in this paper we combine these two aspects within three competing survival models obtaining a data analytic approach that remains computationally feasible in a fully hierarchical bayesian framework using markov chain monte carlo methods we illustrate our proposed methods with an analysis of spatially oriented breast cancer survival data from the surveillance epidemiology and end results program of the national cancer institute our results indicate appreciable advantages for our approach over competing methods that impose unrealistic parametric assumptions ignore spatial association or both

censored regression quantiles using quantile regression to analyze survival times offers an valuable complement to traditional cox proportional hazards modelling unfortunately this approach has been hampered by the lack of a conditional quantile estimator for censored data that is directly analogous to the kaplan meier estimator and applies under standard assumptions for censored regression models here a recursively reweighted estimator of the regression quantile process is developed as a direct generalization of the kaplan meier estimator specifically the asymptotic behavior is directly analogous to that of the kaplan meier estimator and computation is essentially equivalent to current simplex methods for the quantile process in the uncensored case some preliminary examples suggest the strong potential of these methods as a complement to the use of cox models

quantile regression for left truncated semicompeting risks data summary semicompeting risks is often encountered in biomedical studies where a terminating event censors a nonterminating event but not vice versa in practice left truncation on the terminating event may arise and can greatly complicate the regression analysis on the nonterminating event in this work we propose a quantile regression method for left truncated semicompeting risks data which provides meaningful interpretations as well as the flexibility to accommodate varying covariate effects we develop estimation and inference procedures that can be easily implemented by existing statistical software asymptotic properties of the resulting estimators are established including uniform consistency and weak convergence the finite sample performance of the proposed method is evaluated via simulation studies an application to a registry dataset provides an illustration of our proposals