Analyzing count data with measurement error

Journal Article · 07 February 2022 · Quality and Reliability Engineering International

DOI: https://doi.org/10.1002/qre.3078 · OSTI ID:1865025

Hamada, Michael S.  ^[1]; Casleton, Emily M. ^[1]; Osthus, Dave ^[1]; Weaver, Brian P. ^[1]; Steiner, Stefan H. ^[2]

---

1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Univ. of Waterloo, ON (Canada)

In this article, we analyze observed count data such as the number of defects in a steel product where the observed counts are the true counts measured with errors. We account for the measurement error by using a measurement error model based on a latent lognormal (LLN) distribution. We consider making inference about a single population (e.g., from samples of a production lot) and a regression model (e.g., from runs of a designed experiment), where the measurement system properties are known, that is, the parameters of the LLN distribution are known. Then, we consider simultaneous inference for the single population and regression model as well as the measurement system. We demonstrate the proposed methodology with both simulated and real observed counts.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1865025
• Report Number(s): LA-UR-21-28393
• Journal Information: Quality and Reliability Engineering International, Journal Name: Quality and Reliability Engineering International Journal Issue: 5 Vol. 38; ISSN 0748-8017
• Publisher: WileyCopyright Statement
• Country of Publication: United States
• Language: English