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 twofactor 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 Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1352375
 Report Number(s):
 LAUR1628976
Journal ID: ISSN 08982112
 Grant/Contract Number:
 AC5206NA25396
 Resource Type:
 Journal Article: Accepted Manuscript
 Journal Name:
 Quality Engineering
 Additional Journal Information:
 Journal Volume: 29; Journal Issue: 3; Journal ID: ISSN 08982112
 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 AndersonCook, 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., & AndersonCook, Christine Michaela. Quantifying similarity in reliability surfaces using the probability of agreement. United States. doi:10.1080/08982112.2017.1312004.
Stevens, Nathaniel T., and AndersonCook, 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 AndersonCook, 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 twofactor 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}
}

Combining information from different populations to improve precision, simplify future predictions, or improve underlying understanding of relationships can be advantageous when considering the reliability of several related sets of systems. Using the probability of agreement to help quantify the similarities of populations can help to give a realistic assessment of whether the systems have reliability that are sufficiently similar for practical purposes to be treated as a homogeneous population. In addition, the new method is described and illustrated with an example involving two generations of a complex system where the reliability is modeled using either a logistic or probit regressionmore »

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