Bias-Corrected Maximum Likelihood Estimation for the Process Performance Index using Inverse Gaussian Distribution

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 會議論文 > Item 987654321/123465

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/123465

Title:	Bias-Corrected Maximum Likelihood Estimation for the Process Performance Index using Inverse Gaussian Distribution
Authors:	Tsai, Tzong-Ru;Xin, H;Fan, Ya-Yen;Lio, YL
Keywords:	Bias correction;bootstrap methods;Fisher information matrix;inverse Gaussian distribution;maximum likelihood estimation
Date:	2022-08-04
Issue Date:	2023-04-28 18:11:19 (UTC+8)
Publisher:	International Society of Science and Applied Technologies
Abstract:	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
Relation:	27th ISSAT International Conference on Reliability and Quality in Design, p.86-90
Appears in Collections:	[統計學系暨研究所] 會議論文

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