title: Optimal sequential Bayesian analysis for degradation tests.
abstract: Degradation tests are especially difficult to conduct for items with high reliability. Test costs, caused mainly by prolonged item duration and item destruction costs, establish the necessity of sequential degradation test designs. We propose a methodology that sequentially selects the optimal observation times to measure the degradation, using a convenient rule that maximizes the inference precision and minimizes test costs. In particular our objective is to estimate a quantile of the time to failure distribution, where the degradation process is modelled as a linear model using Bayesian inference. The proposed sequential analysis is based on an index that measures the expected discrepancy between the estimated quantile and its corresponding prediction, using Monte Carlo methods. The procedure was successfully implemented for simulated and real data.
authors: Universidad Autónoma de Aguascalientes, Aguascalientes, Aguascalientes, Mexico.
authors: Silvia
authors: S
authors: Rodríguez-Narciso
authors: Centro de Investigación en Matemáticas, Guanajuato, Mexico. jac@cimat.mx.
authors: J Andrés
authors: JA
authors: Christen
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D016014:Linear Models; D009010:Monte Carlo Method; D015203:Reproducibility of Results
keywords: Bayesian analysis; Degradation tests; Monte Carlo methods; Sequential analysis