Model For Censored Data Research Articles

A semi-parametric estimation of a survival function from incomplete and doubly censored data

The purpose of this paper is to present a semi-parametric estimation of a survival function when analyzing incomplete and doubly censored data. Under the assumption that the chance of censoring is not related to the individual's survivorship, we propose a consistent estimation of survival. The derived estimator treats the uncensored observations nonparametrically and uses parametric models for both right and left censored data. Some asymptotic properties and simulation studies are also presented in order to analyze the behavior of the proposed estimator.

Completeness of time-ordered indicators in censored data models

For random right censoring the completeness of the censoring indicators, ordered according to observation time, is shown.

Residual analysis for parametric models fit to censored data

The focus of this paper is on residual analysis for the lognormal and extreme value or Weibull models, although the proposed methods can be applied to any parametric model. Residuals developed by Barlow and Prentice (1988) for the Cox proportional hazards model are extended to the parametric model setting. Three different residuals are proposed based on this approach with two residuals measuring the impact of survival time and one measuring the impact of the covariates included in the model. In addition, a residual derived from the deviations equality presented in Efron and Johnstone (1990) and the residual proposed by Joergensen (1984) for censored data models are discussed.

A Bayesian Bootstrap for Censored Data

A Bayesian bootstrap for a censored data model is introduced. Its small sample distributional properties are discussed and found to be similar to Efron's bootstrap for censored data. In the absence of censoring, the Bayesian bootstrap for censored data reduces to Rubin's Bayesian bootstrap for complete data. A first-order large-sample theory is developed. This theory shows that both censored data bootstraps are consistent bootstraps for approximating the sampling distribution of the Kaplan-Meier estimator. It also shows that both bootstraps are consistent bootstraps for approximating a posterior distribution of the survival function with respect to each member of the class of conjugate beta-neutral process priors.

A test of independence for the coordinates of bivariate censored data

The bivariate censored data model is studied and a test to see whether the coordinates are independent is given via density estimation methods. This proposed test is distribution free. Comparison with a test using survival functions is discussed. Strong approximation of empirical distributions, kernel-type density estimators and univariate/bivariate product-limit estimators are utilized.

Statistical choice of extremal models for complete and censored data

Abstract Within the framework of the general extreme-value distribution (GEV, with shape parameter θ), which combines the Frechet (θ 0), the power functions of some statistics for rejecting the Gumbel distribution (θ = 0) are compared. Often in checking the suitability of a distribution, a goodness of fit test is used involving the whole range of the distribution whilst the valid interest is in one tail only. The influence of the observations in the other tail on the power of the test is evaluated and the correctness of the distribution in that other tail of the sample is discussed.