Bayesian prediction interval for a constant-stress partially accelerated life test model under censored data
oleh: Showkat Ahmad Lone, Hanieh Panahi, Ismail Shah
| Format: | Article |
|---|---|
| Diterbitkan: | Taylor & Francis Group 2021-01-01 |
Deskripsi
The present communication develops the tools for Bayesian prediction of the Gompertz distribution based on CSPALT. The Metropolis-Hastings algorithm is applied to evaluate the BPIs for a censored sample based on unified hybrid censoring scheme. In order to investigate the impact of methodologies adopted, two numerical examples are performed. The simulated results show that reducing the censoring percentages causes smaller BPIs. The flexibility of the UHCS in evaluating the BPIs can be helped to overcome many difficulties in engineering problems.