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Title: Analyzing degradation data with a random effects spline regression model

This study proposes using a random effects spline regression model to analyze degradation data. Spline regression avoids having to specify a parametric function for the true degradation of an item. A distribution for the spline regression coefficients captures the variation of the true degradation curves from item to item. We illustrate the proposed methodology with a real example using a Bayesian approach. The Bayesian approach allows prediction of degradation of a population over time and estimation of reliability is easy to perform.
Authors:
ORCiD logo [1] ;  [1] ; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-16-28372
Journal ID: ISSN 0898-2112
Grant/Contract Number:
AC52-06NA25396
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
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; basis; Bayesian inference; natural spline; prediction; reliability
OSTI Identifier:
1352372

Fugate, Michael Lynn, Hamada, Michael Scott, and Weaver, Brian Phillip. Analyzing degradation data with a random effects spline regression model. United States: N. p., Web. doi:10.1080/08982112.2017.1307390.
Fugate, Michael Lynn, Hamada, Michael Scott, & Weaver, Brian Phillip. Analyzing degradation data with a random effects spline regression model. United States. doi:10.1080/08982112.2017.1307390.
Fugate, Michael Lynn, Hamada, Michael Scott, and Weaver, Brian Phillip. 2017. "Analyzing degradation data with a random effects spline regression model". United States. doi:10.1080/08982112.2017.1307390. https://www.osti.gov/servlets/purl/1352372.
@article{osti_1352372,
title = {Analyzing degradation data with a random effects spline regression model},
author = {Fugate, Michael Lynn and Hamada, Michael Scott and Weaver, Brian Phillip},
abstractNote = {This study proposes using a random effects spline regression model to analyze degradation data. Spline regression avoids having to specify a parametric function for the true degradation of an item. A distribution for the spline regression coefficients captures the variation of the true degradation curves from item to item. We illustrate the proposed methodology with a real example using a Bayesian approach. The Bayesian approach allows prediction of degradation of a population over time and estimation of reliability is easy to perform.},
doi = {10.1080/08982112.2017.1307390},
journal = {Quality Engineering},
number = 3,
volume = 29,
place = {United States},
year = {2017},
month = {3}
}