Bayesian Estimation of Constant-Stress Life Test Model Using Type-I Censored Data from the Linear Failure Rate Distribution

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.