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Title: 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:
 [1]; ORCiD logo [2]
  1. Univ. of San Francisco, San Francisco, CA (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (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:
Journal Article: 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. doi: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. doi: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}
}

Journal Article:
Free Publicly Available Full Text
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