Estimation and Optimal Plan in Step-Stress Partially Accelerated Life Test Model with Progressive Hybrid Censored Data from Pareto Distribution

Abstract

This paper proposes a step-stress partially accelerated life test model from Pareto lifetime distribution under progressive type-I hybrid censoring. Maximum likelihood estimators (MLEs) of the distribution parameters and acceleration factor are derived by using Newton-Raphson algorithm. In addition, the approximate fisher information matrix is calculated for constructing the approximate confidence intervals of the parameters and acceleration factor. The approximate confidence intervals (ACIs) are derived based on normal approximation to the asymptotic distribution of MLEs. Optimal step-stress partially accelerated life test plan is developed by minimizing the generalized asymptotic variance (GAV) of the MLEs of the model parameters. Finally, a Monte-Carlo simulation study is carried out to illustrate the effectiveness of the proposed methods