Estimation for the generalized Gompertz distribution of hybrid progressive censored samples

Issue title: Mathematical Modelling in Computational and Life Sciences

Guest editors: Ahmed Farouk

Article type: Research Article

Authors: Alrajhi, Sharifah^a | Almarashi, Abdullah M.^a | Algarni, Ali^a | Amein, M.M.^{b; c; *}

Affiliations: [a] Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Kingdom Saudi Arabia | [b] Department of Mathematics and Statistics, Faculty of Science, Taif University, Hawia, Taif | [c] Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt

Correspondence: [*] Corresponding author. M.M. Amein. E-mail: montaser.m.amein@gmail.com.

Abstract: In this paper, Bayesian and non Bayesian methods are adapted to determined interval and point estimations under Type-I hybrid Progressively censored for generalized Gompertz distribution. The special case from MCMC adjust Metropolis-Hastings within Gibbs sampling in the case Bayesian procedure is used to solve the double integrations. Numerical examples including generation data sets are shown to make a conclusion on methods estimations used here.

Keywords: Type-I hybrid Progressive, Bayesian and Non-Bayesian estimations, Markov chain Mont-Carlo, generalized Gompertz distribution

DOI: 10.3233/JIFS-179555

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 3, pp. 2693-2702, 2020