Bias-corrected maximum likelihood estimation and Bayesian inference for the process performance index using inverse Gaussian distri-bution

doi:10.3390/stats5040064

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/123288

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题名:	Bias-corrected maximum likelihood estimation and Bayesian inference for the process performance index using inverse Gaussian distri-bution
作者:	Tsai, Tzong-Ru;Xin, Hua;Fan, Ya-Yen;Lio, Yuhlong
关键词:	Bayesian estimation;bootstrap method;maximum likelihood estimation;process capability analysis;process performance index
日期:	2022-11-05
上传时间:	2023-04-28 17:33:23 (UTC+8)
出版者:	MDPI AG
摘要:	In this study, the estimation methods of bias-corrected maximum likelihood (BCML), bootstrap BCML (B-BCML) and Bayesian using Jeffrey’s prior distribution were proposed for the inverse Gaussian distribution with small sample cases to obtain the ML and Bayes estimators of the model parameters and the process performance index based on the lower specification process performance index. Moreover, an approximate confidence interval and the highest posterior density interval of the process performance index were established via the delta and Bayesian inference methods, respectively. To overcome the computational difficulty of sampling from the posterior distribution in Bayesian inference, the Markov chain Monte Carlo approach was used to implement the proposed Bayesian inference procedures. Monte Carlo simulations were conducted to evaluate the performance of the proposed BCML, B-BCML and Bayesian estimation methods. An example of the active repair times for an airborne communication transceiver is used for illustration.
關聯:	Stats 5(4), p.1079-1096
DOI:	10.3390/stats5040064
显示于类别:	[統計學系暨研究所] 期刊論文

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