Inferences for Exponentiated Gamma Constant-Stress Partially Accelerated Life Test Model Based on Generalized Type-I Hybrid Censored Data

Abdalla Rabie; Abd-El-Baset A Ahmad; Thierno Souleymane Barry; Hassan M Aljohani; Nada M Alfaer; Abdulaziz S Alghamdi

doi:10.1155/2021/5424630

Inferences for Exponentiated Gamma Constant-Stress Partially Accelerated Life Test Model Based on Generalized Type-I Hybrid Censored Data

Comput Intell Neurosci. 2021 Dec 27:2021:5424630. doi: 10.1155/2021/5424630. eCollection 2021.

Authors

Abdalla Rabie¹, Abd-El-Baset A Ahmad², Thierno Souleymane Barry³, Hassan M Aljohani⁴, Nada M Alfaer⁴, Abdulaziz S Alghamdi⁵

Affiliations

¹ Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut 71524, Egypt.
² Department of Mathematics, Faculty of Science, Assiut University, Assiut, Egypt.
³ Mathematics (Statistics Option) Program, Pan African University, Institute of Basic Sciences, Technology and Innovation (PAUISTI), P. O. BOX 62000-00200, Nairobi, Kenya.
⁴ Department of Mathematics & Statistics, College of Science, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia.
⁵ Department of Mathematics, College of Science & Arts, King Abdulaziz University, P. O. Box 344, Rabigh 21911, Saudi Arabia.

Abstract

In this paper, the exponentiated gamma distribution (EGD) with generalized Type-I hybrid censored data under constant-stress partially accelerated life test (CSPALT) model is considered. The Bayesian and E-Bayesian estimation methods, as well as the maximum likelihood estimation method, are discussed for the parameter of the distribution and the acceleration factor. The E-Bayesian and Bayesian estimates are derived by using the squared error loss (SEL) and the LINEX loss functions. The MCMC method is applied for deriving the Bayesian and then E-Bayesian estimates. Moreover, a real data set is given for the illustrative purpose. After all, an evaluation is performed for the results of the proposed methods.

MeSH terms

Bayes Theorem*