Monte Carlo Methods for Bayesian Inference on the Linear Hazard Rate Distribution

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Title:	Monte Carlo Methods for Bayesian Inference on the Linear Hazard Rate Distribution
Authors:	林千代;Lin, Chien-tai;Wu, Sam J. S.;Balakrishnan, N.
Contributors:	淡江大學數學學系
Keywords:	Bayesian computation;General progressive Type-II censoring;Markov Chain Monte Carlo (MCMC) method;Prediction;Simulation
Date:	2006-09
Issue Date:	2010-08-10 09:59:34 (UTC+8)
Publisher:	Taylor & Francis
Abstract:	The Bayesian estimation and prediction problems for the linear hazard rate distribution under general progressively Type-II censored samples are considered in this article. The conventional Bayesian framework as well as the Markov Chain Monte Carlo (MCMC) method to generate the Bayesian conditional probabilities of interest are discussed. Sensitivity of the prior for the model is also examined. The flood data on Fox River, Wisconsin, from 1918 to 1950, are used to illustrate all the methods of inference discussed in this article.
Relation:	Communications in Statistics: Simulation and Computation 35(3), pp.575-590
DOI:	10.1080/03610910600716647
Appears in Collections:	[Graduate Institute & Department of Mathematics] Journal Article

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