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In collecting clinical data, data would be censored due to competing risks or patient withdrawal. The statistical inference for censoring data is always based on the assumption that the failure time and censoring time is independent. But in practice the failure time and censoring time are often dependent. Dependent censoring make the job to deal with censoring data more complicated. In this paper, we assume that the joint distribution of the failure time variable and censoring time variable is a function of their marginal distributions. This function is called a copula. Under prespecified copulas, the maximum likelihood estimators for cox proportional hazards models are worked out. Statistical analysis results are carried by simulations. When dependent censoring happens, the proposed method will do better than the traditional method used in independent situations. Simulation results show that the proposed method can get efficient estimations.     

A note on estimation of the mean residual life function with left-truncated and right-censored data

This note focuses on estimating the mean residual life function with left-truncated and right-censored data. We show that the proposed estimator converges weakly to a Gaussian process. The performances of the estimator and its pointwise confidence intervals are illustrated through simulation studies.     

风险率函数基于右删失数据的估计

在随机右删失数据情形下,对风险率函数给出了一种类似于完全样本下由Rosenblatt提出的估计.并在较弱的条件下,讨论了K-类密度估计的强收敛速度和渐近性;同时给出了风险率函数估计量的强收敛速度.     

Simple-Tree Weighted Logrank Tests for Right-Censored Data

In this paper we are concerned with the problem of testing against the simple-tree alternative that there is at least one treatment more effective than the control when data are subject to random right-censorship. A class of tests based on linear combinations of two-sample weighted logrank statistics each comparing an individual treatment with the control is proposed. Asymptotic relative efficiencies of the simple-tree versions of Gehan-Wilcoxon, logrank and Peto-Prentice-Wilcoxon under Lehmann and scale alternatives are evaluated for various combinations of survival distributions and censoring probabilities. The results of a Monte Carlo level and power study are presented. An illustrated numerical example is also reported.     

右删失时间序列AR模型参数估计

通过Kaplan-Meier估计和Nelson-Aalen估计得到了平稳时间序列被另一平稳序列右删失下.AR模型的参数估计.首先,通过与完全数据下的参数估计进行对比,说明了两种估计方法的效果.然后,根据计算机模拟的样本量以及删失率的不同,对比了两种估计的优劣,并且模拟结果表明两种估计是有效的.