Hypothesis Testing and Interval Estimation on Common Hazard Rate of Two Exponential Populations

Abstract

This paper discusses the hypothesis testing and interval estimation on the common hazard rate parameter of two exponential populations with different locations. In the case of hypothesis testing, we derived test procedures using a generalized p-value approach, parametric bootstrap likelihood ratio approach, and computational approach.
In the case of interval estimation, we derived generalized, bootstrap, and highest posterior density (HPD) intervals. The test procedures are evaluated using size and power values, whereas the interval estimators are evaluated using coverage probability and average length. Finally, we demonstrate the potential applicability of the current model problem using real-life datasets.