On progressively censored competing risks data for Weibull distributions

Authors:
Bhuvanesh Pareek;Debasis Kundu;Sumit Kumar
Affiliations:
Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Pin 208016, India;Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Pin 208016, India;Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Pin 208016, India
Venue:
Computational Statistics & Data Analysis
Year:
2009

Citing 2
Cited 3

Estimation of parameters from progressively censored data using EM algorithm

Computational Statistics & Data Analysis
Fisher information based progressive censoring plans

Computational Statistics & Data Analysis

Progressively Type-II censored competing risks data from Lomax distributions

Computational Statistics & Data Analysis
Progressively first-failure censored reliability sampling plans with cost constraint

Computational Statistics & Data Analysis
Inference for accelerated competing failure models from Weibull distribution under Type-I progressive hybrid censoring

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	0.03

Visualization

Abstract

In survival analysis, or in reliability study, an investigator is often interested in the assessment of a specific risk in the presence of other risk factors. It is well known as the competing risks problem in statistical literature. Moreover, censoring is inevitable in any life testing or reliability study. In this paper, we consider a very general censoring scheme, namely a progressive censoring scheme. It is further assumed that the lifetime distribution of the individual causes are independent and Weibull-distributed with the same shape parameters but different scale parameters. We obtain the maximum likelihood and approximate maximum likelihood estimates of the unknown parameters. We also compute the observed Fisher information matrix using the missing information principles, and use them to compute the asymptotic confidence intervals. Monte Carlo simulations are performed to compare the performances of the different methods, and one data set is analyzed for illustrative purposes. We also discuss different optimality criteria, and selected optimal progressive censoring plans are presented.