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This paper considers both likelihood and Bayesian estimations of a constant-stress partially accelerated life test model with type-I censored data from the linear failure rate distribution. The maximum likelihood estimates of the model parameters are obtained using Newton–Raphson technique. The posterior means and posterior variances are obtained under the squared error loss function using Lindley's approximation procedure. The advantages of this approximation are exposed. Monte Carlo simulations are prepared under different sizes of samples and different parameter values for comparing and evaluating the proposed methods of estimation. [ABSTRACT FROM AUTHOR]

In this article, the case of failure-censored constant-stress partially accelerated life test for highly reliable materials or devices assuming the linear failure rate distribution is considered. The maximum likelihood estimates of the distribution parameters and the acceleration factor are obtained. In order to evaluate the performance, the mean squared errors are calculated using different sizes of samples. In addition, the approximate confidence intervals of model parameters are constructed. For illustrative purposes, Monte Carlo simulation studies are offered. [ABSTRACT FROM AUTHOR]

The paper considers the case of constant-stress partially accelerated life testing (CSPALT) when two stress levels are involved under type-I censoring. The lifetimes of test items are assumed to follow a two-parameter Pareto lifetime distribution. Maximum-likelihood method is used to estimate the parameters of CSPALT model. Confidence intervals for the model parameters are constructed. Optimum CSPALT plans that determine the best choice of the proportion of test units allocated to each stress are developed. Such optimum test plans minimize the generalized asymptotic variance of the maximum-likelihood estimators of the model parameters. For illustration, Monte Carlo simulation studies are presented. [ABSTRACT FROM AUTHOR]