Quantifying similarity in reliability surfaces using the probability of agreement
Abstract
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.
- Authors:
-
- Univ. of San Francisco, San Francisco, CA (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1352375
- Report Number(s):
- LA-UR-16-28976
Journal ID: ISSN 0898-2112
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Quality Engineering
- Additional Journal Information:
- Journal Volume: 29; Journal Issue: 3; Journal ID: ISSN 0898-2112
- Publisher:
- American Society for Quality Control
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Mathematics
Citation Formats
Stevens, Nathaniel T., and Anderson-Cook, Christine Michaela. Quantifying similarity in reliability surfaces using the probability of agreement. United States: N. p., 2017.
Web. doi:10.1080/08982112.2017.1312004.
Stevens, Nathaniel T., & Anderson-Cook, Christine Michaela. Quantifying similarity in reliability surfaces using the probability of agreement. United States. https://doi.org/10.1080/08982112.2017.1312004
Stevens, Nathaniel T., and Anderson-Cook, Christine Michaela. Thu .
"Quantifying similarity in reliability surfaces using the probability of agreement". United States. https://doi.org/10.1080/08982112.2017.1312004. https://www.osti.gov/servlets/purl/1352375.
@article{osti_1352375,
title = {Quantifying similarity in reliability surfaces using the probability of agreement},
author = {Stevens, Nathaniel T. and Anderson-Cook, Christine Michaela},
abstractNote = {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.},
doi = {10.1080/08982112.2017.1312004},
journal = {Quality Engineering},
number = 3,
volume = 29,
place = {United States},
year = {Thu Mar 30 00:00:00 EDT 2017},
month = {Thu Mar 30 00:00:00 EDT 2017}
}
Web of Science
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Works referencing / citing this record:
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
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