Modelling accelerated life test data by using a Bayesian approach

被引:4
|
作者
Sinha, D
Patra, K
Dey, DK
机构
[1] Univ Connecticut, Coll Liberal Arts & Sci, Dept Stat, Storrs, CT 06269 USA
[2] Harvard Univ, Sch Publ Hlth, Boston, MA 02115 USA
[3] Med Univ S Carolina, Charleston, SC 29425 USA
关键词
conditional predictive ordinate; latent risk; limited failure population; Markov chain Monte Carlo methods;
D O I
10.1111/1467-9876.00402
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Because of the high reliability of many modern products, accelerated life tests are becoming widely used to obtain timely information about their time-to-failure distributions. We propose a general class of accelerated life testing models which are motivated by the actual failure process of units from a limited failure population with a positive probability of not failing during the technological lifetime. We demonstrate a Bayesian approach to this problem, using a new class of models with non-monotone hazard rates, the hazard model with potential scope for use far beyond accelerated life testing. Our methods are illustrated with the modelling and analysis of a data set on lifetimes of printed circuit boards under humidity accelerated life testing.
引用
收藏
页码:249 / 259
页数:11
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