skip to main content

DOE PAGESDOE PAGES

Title: A Bayesian approach to multivariate measurement system assessment

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.
Authors:
 [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-14-28562
Journal ID: ISSN 0022-4065
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Journal of Quality Technology
Additional Journal Information:
Journal Volume: 48; Journal Issue: 3; Journal ID: ISSN 0022-4065
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; gauge R & R study; Markov-chain Monte Carlo; multivariate normal distribution; random effects; repeatability; reproducibility; uncertainty; variance components; scaled inverse Wishart Prior Distribution
OSTI Identifier:
1331257

Hamada, Michael Scott. A Bayesian approach to multivariate measurement system assessment. United States: N. p., Web.
Hamada, Michael Scott. A Bayesian approach to multivariate measurement system assessment. United States.
Hamada, Michael Scott. 2016. "A Bayesian approach to multivariate measurement system assessment". United States. doi:. https://www.osti.gov/servlets/purl/1331257.
@article{osti_1331257,
title = {A Bayesian approach to multivariate measurement system assessment},
author = {Hamada, Michael Scott},
abstractNote = {This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.},
doi = {},
journal = {Journal of Quality Technology},
number = 3,
volume = 48,
place = {United States},
year = {2016},
month = {7}
}