文章基本信息

标题：Bayesian Approach to Ranking and Selection for a Binary Measurement System
本地全文：下载
作者：Mark Eschmann ; James D. Stamey ; Phil D. Young 等
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2019
卷号：9
期号：4
页码：436-444
DOI：10.4236/ojs.2019.94029
出版社：Scientific Research Publishing
摘要：Binary measurement systems that classify parts as either pass or fail are widely used. Inspectors or inspection systems are often subject to error. The error rates are unlikely to be identical across inspectors. We propose a random effects Bayesian approach to model the error probabilities and overall conforming rate. We also introduce a feature-subset selection procedure to determine the best inspector in terms of overall classification accuracy. We provide simulation studies that demonstrate the viability of our proposed estimation ranking and subset-selection methods and apply the methods to a real data set..
关键词：Bayesian Statistics;Quality Control;Binary Measurement Systems;Misclassification