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Inference based on type-ii hybrid censored data from a weibull distribution (2007).
| Content Provider | CiteSeerX |
|---|---|
| Author | Banerjee, Aveek Kundu, Debasis |
| Abstract | Abstract—A hybrid censoring scheme is a mixture of Type-I and Type-II censoring schemes. This article presents the statistical in-ferences on Weibull parameters when the data are Type-II hybrid censored. The maximum likelihood estimators, and the approxi-mate maximum likelihood estimators are developed for estimating the unknown parameters. Asymptotic distributions of the max-imum likelihood estimators are used to construct approximate con-fidence intervals. Bayes estimates, and the corresponding highest posterior density credible intervals of the unknown parameters, are obtained using suitable priors on the unknown parameters, and by using Markov Chain Monte Carlo techniques. The method of obtaining the optimum censoring scheme based on the maximum information measure is also developed. We perform Monte Carlo simulations to compare the performances of the different methods, and we analyse one data set for illustrative purposes. Index Terms—Approximate maximum likelihood estimators, asymptotic distribution, Bayes estimators, hybrid censoring, Markov chain Monte Carlo, maximum likelihood estimators, optimum censoring scheme, Type-I censoring, Type-II censoring. ACRONYM1 MLE maximum likelihood estimator AMLE approximate maximum likelihood estimator CDF cumulative distribution function PDF probability density function K-S Kolmogorov-Smirnov EM expectation maximization w.r.t with respect to r.h.s. right hand side HPD highest posterior density MCMC Markov chain Monte Carlo MSE mean squared error BEL Bayes estimates using Lindley’s approximation BEM Bayes estimates using MCMC NOTATION-th ordered failure time when the sample size is |
| File Format | |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Weibull Distribution Unknown Parameter Type-ii Hybrid Censored Data Asymptotic Distribution Maximum Likelihood Estimator Approxi-mate Maximum Likelihood Estimator Max-imum Likelihood Estimator Posterior Density Credible Interval Sample Size Mcmc Notation-th Type-ii Censoring Lindley Approximation Bem Bayes Bayes Estimator Different Method Error Bel Bayes Maximum Information Measure Hybrid Censoring Hybrid Censoring Scheme Right Hand Side Hpd Statistical In-ferences Bayes Estimate Type-ii Censoring Scheme Markov Chain Monte Carlo Approximate Con-fidence Interval Optimum Censoring Scheme Data Set Suitable Prior Weibull Parameter Illustrative Purpose Markov Chain Monte Carlo Technique Type-i Censoring Failure Time |
| Content Type | Text |
| Resource Type | Article |
