Estimation for the generalized Gompertz distribution of hybrid progressive censored samples

被引：1
|
作者：
Alrajhi, Sharifah [1 ]
Almarashi, Abdullah M. [1 ]
Algarni, Ali [1 ]
Amein, M. M. [2 ,3 ]
机构：
[1] King Abdulaziz Univ, Fac Sci, Dept Stat, Jeddah, Saudi Arabia
[2] Taif Univ, Fac Sci, Dept Math & Stat, Hawia, Taif, Saudi Arabia
[3] Al Azhar Univ, Fac Sci, Dept Math, Cairo, Egypt
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2020年 / 38卷 / 03期
关键词：
Type-I hybrid Progressive; Bayesian and Non-Bayesian estimations; Markov chain Mont-Carlo; generalized Gompertz distribution; EXACT LIKELIHOOD INFERENCE; EXPONENTIAL POPULATIONS;
D O I：
10.3233/JIFS-179555
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：2693 / 2702
页数：10
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