淡江大學機構典藏:Item 987654321/117365
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    題名: Bayesian analysis for lognormal distribution under progressive type-II censoring
    作者: Sukhdev Singh;Yogesh Mani Tripathi;Shuo-Jye Wu
    關鍵詞: equal-tail interval;highest posterior density interval;one-sample prediction;OpenBUGS;two-sample prediction;importance sampling
    日期: 2019-10
    上傳時間: 2019-10-10 12:10:53 (UTC+8)
    出版者: DergiPark Akademik
    摘要: In this paper, we consider the problems of Bayesian estimation and prediction for lognormal distribution under progressive Type-II censored data. We propose various non-informative and informative priors for the unknown lognormal parameters and compute the Bayes estimates under squared error loss function. Importance sampling technique and OpenBUGS are taken into consideration for the computational purpose. Further, we predict lifetimes of both censored and future samples under one- and two-sample prediction frameworks. We also compute the corresponding Bayes predictive bounds. A simulation study is conducted to compare the performance of proposed estimates and a real data set is analyzed to illustrate applications of this study. Finally, a conclusion is presented.
    關聯: Hacettepe Journal of Mathematics and Statistics 48(5), p.1488-1504
    DOI: 10.15672/HJMS.2018.643
    顯示於類別:[統計學系暨研究所] 期刊論文

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