TITLE:
Bayesian Approach to Ranking and Selection for a Binary Measurement System
AUTHORS:
Mark Eschmann, James D. Stamey, Phil D. Young, Dean M. Young
KEYWORDS:
Bayesian Statistics, Quality Control, Binary Measurement Systems, Misclassification
JOURNAL NAME:
Open Journal of Statistics,
Vol.9 No.4,
August
13,
2019
ABSTRACT: 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.