摘要:

A regression method is developed for a general class of functionals. A semiparametric linear model is adopted, and the regression parameters are estimated by maximizing a profiled nonparametric or empirical likelihood based on a local estimate of the conditional distribution function. Simulated and real data examples are shown, including an application of quantile regression to censored survival data from a clinical trial for myeloma.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476492

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data. The proposed estimators do not require full specification of the likelihood. They can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not missing completely at random. These estimators can be used to correct for dependent censoring and nonrandom noncompliance in randomized clinical trials studying the effect of a treatment on the evolution over time of the mean of a response variable. The likelihood-based parametricG-computation algorithm estimator may also be used to attempt to correct for dependent censoring and nonrandom noncompliance. But because of possible model misspecification, the parametricG-computation algorithm estimator, in contrast with the proposed weighted estimators, may be inconsistent for the difference in treatment-arm-specific means, even when compliance is completely at random and censoring is independent. We illustrate our methods with the analysis of the effect of zidovudine (AZT) treatment on the evolution of mean CD4 count with data from an AIDS clinical trial.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476493

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

We consider the efficiency bound for the estimation of the parameters of semiparametric models defined solely by restrictions on the means of a vector of correlated outcomes,Y, when the data onYare missing at random. We show that the semiparametric variance bound is the asymptotic variance of the optimal estimator in a class of inverse probability of censoring weighted estimators and that this bound is unchanged if the data are missing completely at random. For this case we study the asymptotic performance of the generalized estimating equations (GEE) estimators of mean parameters and show that the optimal GEE estimator is inefficient except for special cases. The optimal weighted estimator depends on unknown population quantities. But for monotone missing data, we propose an adaptive estimator whose asymptotic variance can achieve the bound.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476494

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

Consistent estimators for linear and nonlinear regression models with measurement errors in variables in the presence of validation data are proposed. The estimation procedures are based on least squares methods with regression functions replaced by wide-sense conditional expectation functions. The methods do not depend on distributional assumptions and are robust against the misspecification of a measurement error model. They are computationally and analytically simpler than semiparametric methods based on nonparametric regression or density functions.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476495

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

We investigate the extension of the nonparametric regression technique of local polynomial fitting with a kernel weight to generalized linear models and quasi-likelihood contexts. In the ordinary regression case, local polynomial fitting has been seen to have several appealing features in terms of intuitive and mathematical simplicity. One noteworthy feature is the better performance near the boundaries compared to the traditional kernel regression estimators. These properties are shown to carry over to generalized linear model and quasi-likelihood settings. We also derive the asymptotic distributions of the proposed class of estimators that allow for straightforward interpretation and extensions of state-of-the-art bandwidth selection methods.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476496

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

A method of estimating linear model dimension and variable selection is proposed. This new criterion, which generalizes theCpcriterion, the Akaike information criterion (AIC), the Bayes information criterion, and the phiv criterion and is consistent under certain conditions, is based on a new class of penalty functions and a procedure of sorting covariates based ont-statistics. In the course of introducing this method, we discuss the important role of the penalty function in the consistency of model dimension estimation and in variable selection. The proposed method requires less computation than resampling-based methods that search over all subsets of covariates for the true model. Simulation results show that the new method is superior to theCpcriterion and AIC in finite-sample situations as well.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476497

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

In a classical case-control study, Prentice and Pyke proposed to ignore the study design and instead base estimation and inference on a random sampling (i.e., prospective) formulation. We generalize this prospective formulation of case-control studies to include multiplicative models, stratification, missing data, measurement error, robustness, and other examples. The resulting estimators, which ignore the case-control study aspect and instead are based on a random-sampling formulation, are typically consistent for nonintercept parameters and are asymptotically normally distributed. We derive the resulting asymptotic covariance matrix of the parameter estimates. The covariance matrix obtained by ignoring the case-control sampling scheme and using prospective formulas instead is shown to be at worst asymptotically conservative and asymptotically correct in a variety of problems; a simple sufficient condition guaranteeing the latter is obtained.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476498

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

The Theil-Sen estimator of the slope parameter in simple linear regression is extended to data with both the response and the covariate subject to censoring. Based on inverting a suitable version of Kendall's τ statistic, this estimator requires weak assumptions and is simple to compute, and a simple estimate of its asymptotic variance is obtained. A second extension of the Theil-Sen estimator, based on a direct estimation of the median of pairwise slopes, is given. These estimators are compared numerically with versions of Schmitt's estimator and applied to two data sets from the recent astronomical literature.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476499

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

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.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476500

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

Model diagnostic procedures in failure time models using hazard-based residuals rely on the assumption that the residual vector closely resembles a random sample from a unit exponential distribution when the model holds. This article formally investigates the validity of this critical assumption by deriving and examining the properties of parametrically, semiparametrically, and nonparametrically estimated residuals for complete and right-censored data. The joint distribution of the residual vector is characterized, and the behavior of some tests for exponentiality when applied to the residuals is examined analytically and through Monte Carlo methods. Findings reveal that the critical assumption of approximate unit exponentiality of the residual vector may not be viable and, consequently, the model diagnostic procedures considered, which revolve on checking the approximate unit exponentiality of the residual vector (specifically, hazard plotting and the use of spacings and total-time-on-test statistics on the residual vector) may have serious defects. This is especially evident in situations where the failure time distribution is not exponential or when the residuals are obtained nonparametrically in the no-covariate model or semiparametrically in the Cox proportional hazards model.

ISSN:0162-1459    

DOI:10.1080/01621459.1995.10476501

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor