Bias-Corrected Maximum Likelihood Estimation for the Process Performance Index using Inverse Gaussian Distribution
學年 111
學期 1
發表日期 2022-08-04
作品名稱 Bias-Corrected Maximum Likelihood Estimation for the Process Performance Index using Inverse Gaussian Distribution
作品名稱（其他語言）
著者 Tzong-Ru Tsai; H Xin; Ya-Yen Fan; YL Lio
作品所屬單位
出版者
會議名稱 27th ISSAT International Conference on Reliability and Quality in Design
會議地點 線上
摘要 An analytical bias-corrected maximum likelihood estimation procedure and a bootstrap bias-corrected maximum likelihood estimation procedure are proposed for the inverse Gaussian distribution (IGD) to obtain more reliable maximum likelihood estimates (MLEs) of the model parameters and the generalized process capability index (PCI) proposed by Maiti et al. (2010) when the sample size is small. An approximate confidence interval (ACI) of the generalized PCI is obtained for the IGD via using the delta method and the obtained reliable MLEs of the model parameters. Monte Carlo simulations were conducted to evaluate the performance of two proposed estimation methods. Simulation results show that two proposed bias-correction methods outperform the typical maximum likelihood estimation method when the sample size is small in terms of the relative bias and relative mean squared error. © 2022 International Society of Science and Applied Technologies
關鍵字 Bias correction;bootstrap methods;Fisher information matrix;inverse Gaussian distribution;maximum likelihood estimation
語言 en_US
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20220804~20220806
通訊作者
國別 USA
公開徵稿
出版型式
出處 27th ISSAT International Conference on Reliability and Quality in Design, p.86-90
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/123466 )

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