Semiparametric Estimator of Mean Conditional Residual Life Function under Informative Random Censoring from Both Sides

A A Abdushukurov,F A Abdikalikov

doi:10.4236/am.2015.62030

Abstract

In this paper we study estimator of mean residual life function in fixed design regression model when life times are subjected to informative random censoring from both sides. We prove an asymptotic normality of estimators.

Highlights

In survival data analysis, response random variable (r.v.) Z, the survival time of a individual or failure time of a machine that usually can be influenced by r.v
X represents e.g. the dose of a drug for individual or some environmental conditions of a machine. In such practical situations it often occurs that not all of survival times Z1, Zn of n identical objects are complete observed, that they can be censored by other r.v.-s
In this article we consider a regression model in which the response r.v.-s are subjected to random censoring from both sides

Summary

Introduction

Response random variable (r.v.) Z, the survival time of a individual (in medical study) or failure time of a machine (in industrial study) that usually can be influenced by r.v. Let the support of covariate is the interval [0,1] and we describe our regression results in the situation of fixed design points 0 ≤ x1 ≤ x2 ≤ ≤ xn ≤ 1 at which we consider nonnegative independent responses Z1, , Zn . (2015) Semiparametric Estimator of Mean Conditional Residual Life Function under Informative Random Censoring from Both Sides.

Results

Conclusion