Parameter Estimation Under Failure-Censored Constant-Stress Life Testing Model: A Bayesian Approach

Abstract

This article compares likelihood and Bayesian estimations for partially accelerated constant-stress life test model under type II censoring assuming Pareto distribution of the second kind. Both maximum likelihood and Bayesian estimators of the model parameters are derived. The posterior means and posterior variances are obtained under the squared error loss function using Lindley's approximation procedure. The advantages of this proposed procedure are shown. Monte Carlo simulations are conducted under different samples sizes and different parameter values to assess and compare the proposed methods of estimation. A noninformative prior on the model parameters is used to make the comparison more meaningful. It has been observed that Lindley's method usually provides posterior variances and mean squared errors smaller than those of the maximum likelihood estimators. That is, Lindley's method produces improved estimates, which is an advantage of this method.

Keywords:

• Bayesian estimation
• Failure censoring
• Maximum likelihood estimation
• Pareto distribution
• Partially accelerated constant-stress test
• Squared error loss function

Subject Classifications:

• 62N01
• 62N05

ACKNOWLEDGMENTS

The author thanks Professor Nitis Mukhopadhyay, Associate Editor, and the reviewers for their valuable time and useful suggestions to improve the quality of the article.

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