Prediction of Progressive Censored Data from the Gompertz Model

Abstract

Progressive type-II censored sampling is an important scheme of obtaining data in lifetime studies. This article is concerned with the problem of obtaining Bayesian prediction bounds for future order statistics from the two-parameter Gompertz distribution based on progressive type-II censored data using the two-sample prediction technique. An algorithm due to Balakrishnan and Sandhu (Balakrishnan, N., Sandhu, R. A. (1995). A simple algorithm for generating progressive type-II censored samples. American Statistician 49(2):229–230.) and Aggarwala and Balakrishnan (Aggarwala, R., Balakrishnan, N. (1998a). Some properties of progressive censored order statistics from arbitary and uniform distributions with applications to inference and simulation. J. Statist. Plain. Inference 70:35–49.) was used to generate progressive type-II censored samples from the Gompertz distribution and the accuracy of prediction intervals is investigated via Monte Carlo simulation.

Keywords:

• Gompertz model
• Progressive data
• Prior/posterior density
• Predictive density
• Bayesian prediction
• Monte Carlo simulation

Acknowledgment

The author would like to thank the referee for his helpful remarks and suggestions that improved the original manuscript.