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Bayesian estimation and prediction with multiply Type-II censored samples of sequential order statistics from one- and two-parameter exponential distributions

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Bibliographic Details
Title: Bayesian estimation and prediction with multiply Type-II censored samples of sequential order statistics from one- and two-parameter exponential distributions
Authors: Schenk, N.1, Burkschat, M.2 marco.burkschat@ovgu.de, Cramer, E.3, Kamps, U.3
Source: Journal of Statistical Planning & Inference. Apr2011, Vol. 141 Issue 4, p1575-1587. 13p.
Subjects: Bayes' estimation, Sequential analysis, Exponential families (Statistics), Density functionals, Numerical analysis, Parameter estimation, Order statistics
Abstract: Abstract: Based on multiply Type-II censored samples of sequential order statistics, Bayesian estimators are derived for the parameters of one- and two-parameter exponential distributions. In the one-parameter set-up, the posterior density is obtained under the assumption that the prior distribution is given by an inverse Gamma distribution, and the Bayes estimator with respect to squared error loss is calculated. Its performance is illustrated by a numerical example and compared with two non-Bayesian estimators, namely the BLUE and the approximate maximum likelihood estimator (AMLE). Moreover, prediction of future failure times is considered. Minimum risk equivariant estimators and predictors are deduced from the given results. Finally, similar results are presented for the two-parameter situation. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract:Abstract: Based on multiply Type-II censored samples of sequential order statistics, Bayesian estimators are derived for the parameters of one- and two-parameter exponential distributions. In the one-parameter set-up, the posterior density is obtained under the assumption that the prior distribution is given by an inverse Gamma distribution, and the Bayes estimator with respect to squared error loss is calculated. Its performance is illustrated by a numerical example and compared with two non-Bayesian estimators, namely the BLUE and the approximate maximum likelihood estimator (AMLE). Moreover, prediction of future failure times is considered. Minimum risk equivariant estimators and predictors are deduced from the given results. Finally, similar results are presented for the two-parameter situation. [Copyright &y& Elsevier]
ISSN:03783758
DOI:10.1016/j.jspi.2010.11.009