Empirical weighted Bayesian tolerance intervals

ABSTRACT

Bayesian statistics has been widely utilized as an approach that can incorporate prior knowledge into statistical inference. Tolerance intervals (TI) are the most commonly used statistical methods for product quality assurance. There are two main Bayesian approaches for calculating statistical tolerance intervals: Hamada and Wolfinger. A simulation-based approach was implemented to compare two-sided Wolfinger, Hamada, and frequentist tolerance intervals which control the probability content at a specified level of confidence. As sample sizes increase, compared to frequentist, Hamada TI become more conservative while Wolfinger TI are more liberal. To address this issue, we propose an empirical weighted Bayesian TI approach that is a compromise between Hamada and Wolfinger approaches. The proposed Bayesian TI result in narrower limits in certain scenarios while ensuring the confidence content coverage remains comparable to frequentist.

KEYWORDS:

• Bayesian tolerance intervals
• sample size determination
• two-sided tolerance interval
• normal tolerance intervals

Acknowledgments

This research was sponsored by Director of Quality Excellence, David Enck and Product Quality Management, Janssen Pharmaceutical companies of Johnson & Johnson, Titusville, NJ. I thank our colleagues, Craig Bernier, Jyh-Ming Shoung, Qianqiu Li, Luwis Diya from Janssen for their expertise and insight that greatly assisted the research.