Analyzing degradation data with a random effects spline regression model

Fugate, Michael Lynn; Hamada, Michael Scott; Weaver, Brian Phillip

doi:10.1080/08982112.2017.1307390

Analyzing degradation data with a random effects spline regression model

Journal Article · Fri Mar 17 00:00:00 EDT 2017 · Quality Engineering

DOI:https://doi.org/10.1080/08982112.2017.1307390· OSTI ID:1352372

^[1]; Hamada, Michael Scott ^[1]; ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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.

Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1352372

Report Number(s):: LA-UR--16-28372

Journal Information:: Quality Engineering, Journal Name: Quality Engineering Journal Issue: 3 Vol. 29; ISSN 0898-2112

Publisher:: American Society for Quality ControlCopyright Statement

Country of Publication:: United States

Language:: English

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Quality quandaries: Predicting a population of curves Fugate, M.; Hamada, M. S.; Weaver, B. P. Quality Engineering, Vol. 30, Issue 2 https://doi.org/10.1080/08982112.2017.1417601	journal	August 2017

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