Quantifying similarity in reliability surfaces using the probability of agreement

Stevens, Nathaniel T.; Anderson-Cook, Christine Michaela

doi:10.1080/08982112.2017.1312004

Quantifying similarity in reliability surfaces using the probability of agreement

Journal Article · Thu Mar 30 00:00:00 EDT 2017 · Quality Engineering

DOI:https://doi.org/10.1080/08982112.2017.1312004· OSTI ID:1352375

Stevens, Nathaniel T. ^[1]; ^[2]

Univ. of San Francisco, San Francisco, CA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

When separate populations exhibit similar reliability as a function of multiple explanatory variables, combining them into a single population is tempting. This can simplify future predictions and reduce uncertainty associated with estimation. However, combining these populations may introduce bias if the underlying relationships are in fact different. The probability of agreement formally and intuitively quantifies the similarity of estimated reliability surfaces across a two-factor input space. An example from the reliability literature demonstrates the utility of the approach when deciding whether to combine two populations or to keep them as distinct. As a result, new graphical summaries provide strategies for visualizing the results.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1352375

Report Number(s):: LA-UR-16-28976

Journal Information:: Quality Engineering, Journal Name: Quality Engineering Journal Issue: 3 Vol. 29; ISSN 0898-2112

Publisher:: American Society for Quality ControlCopyright Statement

Country of Publication:: United States

Language:: English

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Bayesian probability of predictive agreement for comparing the outcome of two separate regressions: Bayesian probability of predictive agreement for comparing the outcome of two separate regressions Stevens, Nathaniel T.; Rigdon, Steven E.; Anderson-Cook, Christine M. Quality and Reliability Engineering International, Vol. 34, Issue 6 https://doi.org/10.1002/qre.2284	journal	April 2018
How to Host An Effective Data Competition: Statistical Advice for Competition Design and Analysis Anderson‐Cook, Christine M.; Myers, Kary L.; Lu, Lu Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 12, Issue 4 https://doi.org/10.1002/sam.11404	journal	February 2019
Bayesian probability of agreement for comparing the similarity of response surfaces Stevens, Nathaniel T.; Rigdon, Steven E.; Anderson-Cook, Christine M. Journal of Quality Technology https://doi.org/10.1080/00224065.2019.1569961	journal	April 2019
Design and analysis of confirmation experiments Stevens, Nathaniel T.; Anderson-Cook, Christine M. Journal of Quality Technology, Vol. 51, Issue 2 https://doi.org/10.1080/00224065.2019.1571344	journal	April 2019

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