Strictly Positive Estimates of Variance Components for Measurement Systems Analysis Models

• Activity Number: 140
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Quality and Productivity Section
• Author(s): Laura Lancaster* and Christopher Gotwalt
• Companies: SAS Institute and SAS Institute
• Keywords: Variance Components ; Measurement Systems Analysis ; Bayesian ; Random Effects Models

Abstract

Variance components for measurement systems analysis (MSA) models are typically estimated using one of three techniques: the Average and Range Method, Expected Means Squares (EMS), and Restricted Maximum Likelihood (REML). Each of these methods often produce negative variance components estimates. Because negative variance component estimates do not make sense in MSA studies, these estimates are set to zero. Posterior mean estimates using standard non-informative priors such as Jeffrey's prior or a flat prior generate strictly positive variance components but have unacceptably high mean squared errors. We have generalized an alternative non-informative prior method that was developed by Portnoy and Sahai in the 1970's to unbalanced data and have applied this method to MSA studies. We will explain this method and show results of simulation experiments that compare the ability of this method to estimate variance components for typical MSA models with REML.