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A dynamic general linear model for inference from accelerated life tests
Author(s) -
Mazzuchi Thomas A.,
Soyer Refik
Publication year - 1992
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/1520-6750(199210)39:6<757::aid-nav3220390603>3.0.co;2-r
Subject(s) - inference , accelerated life testing , computer science , bayesian inference , bayesian probability , linear model , frequentist inference , econometrics , machine learning , artificial intelligence , mathematics , statistics , weibull distribution
We present a new approach for inference from accelerated life tests. Our approach is based on a dynamic general linear model setup which arises naturally from the accelerated life‐testing problem and uses linear Bayesian methods for inference. The advantage of the procedure is that it does not require large numbers of items to be tested and that it can deal with both censored and uncensored data. We illustrate the use of our approach with some actual accelerated life‐test data. © 1992 John Wiley & Sons, Inc.