Bias-corrected maximum likelihood estimation and Bayesian inference for the process performance index using inverse Gaussian distribution
學年 111
學期 1
出版（發表）日期 2022-11-05
作品名稱 Bias-corrected maximum likelihood estimation and Bayesian inference for the process performance index using inverse Gaussian distribution
作品名稱（其他語言）
著者 Tzong-Ru Tsai; Hua Xin; Ya-Yen Fan; Yuhlong Lio
單位
出版者
著錄名稱、卷期、頁數 Stats 5(4), p.1079-1096
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.
關鍵字 Bayesian estimation;bootstrap method;maximum likelihood estimation;process capability analysis;process performance index
語言 en
ISSN 2571-905X
期刊性質 國外
收錄於 ESCI
產學合作
通訊作者
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/123288 )