Analysis of Type-II progressively hybrid censored data

Authors:
Debasis Kundu;Avijit Joarder
Affiliations:
Department of Mathematics, Indian Institute of Technology Kanpur, Kanpur 208016, India;Reserve Bank of India, Mumbai, India
Venue:
Computational Statistics & Data Analysis
Year:
2006

Citing 1
Cited 6

Numerical recipes: the art of scientific computing

Numerical recipes: the art of scientific computing

Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples

Computational Statistics & Data Analysis
Inference for the Weibull distribution with progressive hybrid censoring

Computational Statistics & Data Analysis
Hybrid censoring: Models, inferential results and applications

Computational Statistics & Data Analysis
Estimation using hybrid censored data from a two-parameter distribution with bathtub shape

Computational Statistics & Data Analysis
Inference for a step-stress partially accelerated life test model with an adaptive Type-II progressively hybrid censored data from Weibull distribution

Journal of Computational and Applied Mathematics
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

The mixture of Type-I and Type-II censoring schemes, called the hybrid censoring scheme, is quite common in life-testing or reliability experiments. Recently, Type-II progressive censoring scheme has become quite popular for analyzing highly reliable data. One drawback of the Type-II progressive censoring scheme is that the length of the experiment can be quite large. In this paper, we introduce a Type-II progressively hybrid censoring scheme, where the experiment terminates at a pre-specified time. For this censoring scheme, we analyze the data under the assumptions that the lifetimes of the different items are independent and exponentially distributed random variables with parameter @l. We obtain the maximum-likelihood estimator of the unknown parameter in an exact form. Asymptotic confidence intervals based on @l@^, ln@l@^, confidence interval based on likelihood ratio test and two bootstrap confidence intervals are also proposed. Bayes estimate and credible interval of the unknown parameter are obtained under the assumption of gamma prior of the unknown parameter. Different methods have been compared using Monte Carlo simulations. One real data set has been analyzed for illustrative purposes.