Abstract

This paper discusses the design of the optimal SSALT plan using Bayesian approach and progressive Type-I right censoring for an exponential life distribution under large sample size and small censoring proportion. The cumulative exposure model and the exponential life distribution in both steps are assumed.  The progressive Type-I right censoring can reduce the cost of the test. This reduction, unfortunately, comes on the expense of reducing the precision of the test. The optimal test parameters, the stress changing time and the first step stress, are obtained by minimizing the expected variance of the life for the p^th percentile using Bayesian approach. A comparison between conventional Type-I and progressive Type-I right censoring is also provided. The results showed that progressive Type-I right censoring is recommended when strong prior information for the model parameters is used as the test precision becomes less sensitive to the censoring proportion.

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