關鍵字查詢 | 類別:期刊論文 | | 關鍵字:Inference of δ = P(X < Y

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序號 學年期 教師動態
1 108/1 統計系 蔡宗儒 教授 期刊論文 發佈 Bayesian Inference of δ = P(X < Y) for Burr Type XII distribution based on progressively first failure-censored samples , [108-1] :Bayesian Inference of δ = P(X < Y) for Burr Type XII distribution based on progressively first failure-censored samples期刊論文Bayesian Inference of δ = P(X < Y) for Burr Type XII distribution based on progressively first failure-censored samplesJessie Marie Byrnes; Yu-Jau Lin; Tzong-Ru Tsai; Yuhlong Liogibbs sampling;Markov Chain Monte Carlo;maximum likelihood estimation;Metropolis–Hastings algorithm;progressive first failure-censoring schemeMathematics 7(9), 794(24 pages)Let X and Y follow two independent Burr type XII distributions and δ=P(X<Y) . If X is the stress that is applied to a certain component and Y is the strength to sustain the stress, then δ is called the stress–strength parameter. In this study, The Bayes estimator of δ is investigated based on a progressively first failure-censored sample. Because of computation complexity and no closed form for the estimator as well as posterior distributions, the Markov Chain Monte Carlo procedure using the Metropolis–Hastings algorithm
2 105/2 統計系 蔡宗儒 教授 期刊論文 發佈 Inference of δ = P(X < Y) for Burr XII distributions with record samples , [105-2] :Inference of δ = P(X < Y) for Burr XII distributions with record samples期刊論文Inference of δ = P(X < Y) for Burr XII distributions with record samplesJyun-You Chiang; Nan Jiang; Tzong-Ru Tsai; Y. L. LioBootstrap-t method;Fisher information;Log-logistic distribution;Maximum likelihood estimation;Percentile bootstrap methodCommunications in Statistics - Simulation and Computation 47(3), p.822-838Let X and Y follow independent Burr type XII distributions, which share a common inner shape parameter. The maximum likelihood estimator of the parameter δ = P(X < Y) is studied based on record samples. The existence and uniqueness of the maximum likelihood estimator of δ based on record samples are established. When the inner shape parameter is known, an exact confidence interval of δ is derived; otherwise, the Fisher information matrix and two bootstrap methods are used to obtain three approximate confidence intervals of δ. The performances of the proposed methods are evaluated via Monte Carlo si
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