Inference under progressive Type-I interval censoring

Soumya Roy, Gijo E. V. and Biswabrata Pradhan
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Soumya Roy: Indian Institute of Management Kozhikode
Gijo E. V.: Indian Statistical Institute, Bangalore
Biswabrata Pradhan: Indian Statistical Institute, Kolkata

No 191, Working papers from Indian Institute of Management Kozhikode

Abstract: This article considers inference for the unknown parameters of log-normal distribution based on progressive Type-I interval censored data by both frequentist and Bayesian methods. The maximum likelihood estimates (MLE) are computed by using EM algorithm. The asymptotic standard errors (ASEs) of the MLEs are obtained. Various Bayes estimates of the unknown parameters are also computed. It is observed that the Bayes estimates cannot be obtained in explicit form. A Gibbs sampling scheme is developed by adopting a data augmentation method to compute the Bayes estimates and highest posterior density credible intervals. The performance of the MLEs and the Bayesian estimators is judged by a simulation study. A real data set is analyzed for the purpose of illustration.

Keywords: Bayesian D- and C-optimality criteria; Data Augmentation; EM algorithm; Missing Information Principle; Gibbs Sampling; Optimal design. (search for similar items in EconPapers)
Pages: 3 pages
Date: 2016-01
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:iik:wpaper:191

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