Inference for the process performance index of products on the basis of power-normal distribution

doi:10.3390/math10010035

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 987654321/121820

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題名:	Inference for the process performance index of products on the basis of power-normal distribution
作者:	J. Zhu;H Xin;C Zheng;Tzong-Ru Tsai
關鍵詞:	bootstrap methods;maximum likelihood estimation;Monte Carlo simulation;process performance index;power-normal distribution;quality control
日期:	2021-12-23
上傳時間:	2021-12-29 12:10:46 (UTC+8)
摘要:	The process performance index (PPI) can be a simple metric to connect the conforming rate of products. The properties of the PPI have been well studied for the normal distribution and other widely used lifetime distributions, such as the Weibull, Gamma, and Pareto distributions. Assume that the quality characteristic of product follows power-normal distribution. Statistical inference procedures for the PPI are established. The maximum likelihood estimation method for the model parameters and PPI is investigated and the exact Fisher information matrix is derived. We discuss the drawbacks of using the exact Fisher information matrix to obtain the confidence interval of the model parameters. The parametric bootstrap percentile and bootstrap bias-corrected percentile methods are proposed to obtain approximate confidence intervals for the model parameters and PPI. Monte Carlo simulations are conducted to evaluate the performance of the proposed methods. One example about the flow width of the resist in the hard-bake process is used for illustration.
關聯:	Mathematics 10(1), p.35
DOI:	10.3390/math10010035
顯示於類別:	[統計學系暨研究所] 期刊論文

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