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Abstract
In accelerated life tests (ALTs), test units are run at higher stress levels in order to experience more failures. A Weibull regression model can, in this case, be used to infer failure behavior at the normal stress level. In practice, statistical analyses usually do not take batch differences into account even when they are present. To correctly include batch differences in the analysis, one needs to use a regression model with random effects. In this paper, we use such a model to show that ignoring batch differences in modeling can result in overly precise estimates of quantiles and probabilities of failure at the normal stress level, as well as overly precise predictions of the failure time for a new unit at the normal stress level.
Additional information
Notes on contributors
Ramón V. León
Dr. León is Associate Professor in the Department of Statistics, Operations and Management Science. His email address is [email protected].
Yanzhen Li
Mr. Li is a doctoral student in the Department of Industrial and Information Engineering. His email address is [email protected].
Frank M. Guess
Dr. Guess is Professor in the Department of Statistics, Operations and Management Science. His email address is [email protected].
Rapinder S. Sawhney
Dr. Sawhney is Associate Professor in the Department of Industrial and Information Engineer. He is a member of ASQ. His email address is [email protected].
