Inference on Nadarajah-Haghighi distribution with constant stress partially accelerated life tests under progressive type-II censoring

Sanku Dey; Liang Wang; Mazen Nassar

doi:10.1080/02664763.2021.1928014

Inference on Nadarajah-Haghighi distribution with constant stress partially accelerated life tests under progressive type-II censoring

J Appl Stat. 2021 May 18;49(11):2891-2912. doi: 10.1080/02664763.2021.1928014. eCollection 2022.

Authors

Sanku Dey¹, Liang Wang^{2

3}, Mazen Nassar^{4

5}

Affiliations

¹ Department of Statistics, St. Anthony's College, Shillong, India.
² School of Mathematics, Yunnan Normal University, Kunming, People's Republic of China.
³ School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, People's Republic of China.
⁴ Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Kingdom of Saudia Arabia.
⁵ Department of Statistics, Faculty of Commerce, Zagazig University, Zagazig, Egypt.

Abstract

This paper presents methods of estimation of the parameters and acceleration factor for Nadarajah-Haghighi distribution based on constant-stress partially accelerated life tests. Based on progressive Type-II censoring, Maximum likelihood and Bayes estimates of the model parameters and acceleration factor are established, respectively. In addition, approximate confidence interval are constructed via asymptotic variance and covariance matrix, and Bayesian credible intervals are obtained based on importance sampling procedure. For comparison purpose, alternative bootstrap confidence intervals for unknown parameters and acceleration factor are also presented. Finally, extensive simulation studies are conducted for investigating the performance of the our results, and two data sets are analyzed to show the applicabilities of the proposed methods.

Keywords: 62F15; Bayes estimates; Maximum likelihood estimator; Primary 62F10; Secondary 62N02; bootstrap confidence intervals; credible intervals.

Grants and funding

This work of Liang Wang was supported by National Natural Science Foundation of China [grant number 12061091 and China Postdoctoral Science Foundation [grant number 2019M650260].