淡江大學機構典藏:Item 987654321/41190
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    题名: Monte Carlo Methods for Bayesian Inference on the Linear Hazard Rate Distribution
    作者: 林千代;Lin, Chien-tai;Wu, Sam J. S.;Balakrishnan, N.
    贡献者: 淡江大學數學學系
    关键词: Bayesian computation;General progressive Type-II censoring;Markov Chain Monte Carlo (MCMC) method;Prediction;Simulation
    日期: 2006-09
    上传时间: 2010-08-10 09:59:34 (UTC+8)
    出版者: Taylor & Francis
    摘要: 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.
    關聯: Communications in Statistics: Simulation and Computation 35(3), pp.575-590
    DOI: 10.1080/03610910600716647
    显示于类别:[數學學系暨研究所] 期刊論文

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