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Stamey, J.A., Bratcher, T.L. and Young, D.M. (2004) Parameter Subset Selection and Multiple Comparisons of Poisson Rate Parameters with Misclassification. Computational Statistics & Data Analysis, 45, 467-479.
https://doi.org/10.1016/S0167-9473(03)00068-9

has been cited by the following article:

• 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.