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Title: Comparing the reliability of related populations with the probability of agreement

Abstract

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 regression model. Note that supplementary materials including code, datasets, and added discussion are available online.

Authors:
 [1];  [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.:
USDOD; USDOE
OSTI Identifier:
1304827
Report Number(s):
LA-UR-15-27900
Journal ID: ISSN 0040-1706
Grant/Contract Number:
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Technometrics
Additional Journal Information:
Journal Name: Technometrics; Journal ID: ISSN 0040-1706
Publisher:
Taylor & Francis
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; generalized linear models; reliability; equivalence testing; homogeneity of population characteristics; probability of agreement

Citation Formats

Stevens, Nathaniel T., and Anderson-Cook, Christine M. Comparing the reliability of related populations with the probability of agreement. United States: N. p., 2016. Web. doi:10.1080/00401706.2016.1214180.
Stevens, Nathaniel T., & Anderson-Cook, Christine M. Comparing the reliability of related populations with the probability of agreement. United States. doi:10.1080/00401706.2016.1214180.
Stevens, Nathaniel T., and Anderson-Cook, Christine M. 2016. "Comparing the reliability of related populations with the probability of agreement". United States. doi:10.1080/00401706.2016.1214180. https://www.osti.gov/servlets/purl/1304827.
@article{osti_1304827,
title = {Comparing the reliability of related populations with the probability of agreement},
author = {Stevens, Nathaniel T. and Anderson-Cook, Christine M.},
abstractNote = {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 regression model. Note that supplementary materials including code, datasets, and added discussion are available online.},
doi = {10.1080/00401706.2016.1214180},
journal = {Technometrics},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 7
}

Journal Article:
Free Publicly Available Full Text
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  • 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 formore » visualizing the results.« less
  • The author compared a recursive digital filter proposed as a detection method for French special nuclear material monitors with the author's detection methods, which employ a moving-average scaler or a sequential probability-ratio test. Each of these nine test subjects repeatedly carried a test source through a walk-through portal monitor that had the same nuisance-alarm rate with each method. He found that the average detection probability for the test source is also the same for each method. However, the recursive digital filter may have on drawback: its exponentially decreasing response to past radiation intensity prolongs the impact of any interference frommore » radiation sources of radiation-producing machinery. He also examined the influence of each test subject on the monitor's operation by measuring individual attenuation factors for background and source radiation, then ranked the subjects' attenuation factors against their individual probabilities for detecting the test source. The one inconsistent ranking was probably caused by that subject's unusually long stride when passing through the portal.« less
  • In this paper, we compare statistical methods for analyzing pass/fail data collected over time; some methods are traditional and one (the RADAR or Rationale for Assessing Degradation Arriving at Random) was recently developed. These methods are used to provide uncertainty bounds on reliability. We make observations about the methods' assumptions and properties. Finally, we illustrate the differences between two traditional methods, logistic regression and Weibull failure time analysis, and the RADAR method using a numerical example.