Estimation in the Complementary Exponential Geometric Distribution Based on Progressive Type-II Censored Data  

作　　者：Ozlem GürünlüAlma Reza Arabi Belaghi 

机构地区：[1]Department of Statistics,Faculty of Sciences,Muğla SıtkıKoçman University,Muğla,Turkey [2]Department of Statistics,Faculty of Mathematical Sciences,University of Tabriz,Tabriz,Iran

出　　处：《Communications in Mathematics and Statistics》2020年第4期409-441,共33页数学与统计通讯（英文）

摘　　要：Complementary exponential geometric distribution has many applications in survival and reliability analysis.Due to its importance,in this study,we are aiming to estimate the parameters of this model based on progressive type-II censored observations.To do this,we applied the stochastic expectation maximization method and Newton-Raphson techniques for obtaining the maximum likelihood estimates.We also considered the estimation based on Bayesian method using several approximate:MCMC samples,Lindely approximation and Metropolis-Hasting algorithm.In addition,we considered the shrinkage estimators based on Bayesian and maximum likelihood estimators.Then,the HPD intervals for the parameters are constructed based on the posterior samples from the Metropolis-Hasting algorithm.In the sequel,we obtained the performance of different estimators in terms of biases,estimated risks and Pitman closeness via Monte Carlo simulation study.This paper will be ended up with a real data set example for illustration of our purpose.

关 键 词：Bayesian analysis Complementary exponential geometric(CEG)distribution Progressive type-II censoring Maximum likelihood estimators SEM algorithm Shrinkage estimator 

分 类 号：O17[理学—数学]