Asymptotic theory for the MLE from randomly censored exponential samples

作     者：WANG Qihua Department of Probability and Statistics, Peking University, Beijing 100871, China 

作者机构：Department of Probability and Statistics Peking University Beijing China 

出 版 物：《Chinese Science Bulletin》 (科学通报(英文版))

年 卷 期：1998年第43卷第13期

页      面：1071-1076页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：thePostdoctoralScienceFoundationofChina 

主　　题：maximum likelihood estimate Edgeworth expansion bootstrap approximation asymptotic minimax efficiency law of iterated logarithm. 

摘      要：The MLE of the parameter of the exponential population from the censored observations is considered. The Edgeworth expansions for the Studentized MLE are established by representing the relevant statistic as a U\|statistic plus a remainder. A semiparametric bootstrap method is introduced to the random censored model and the accuracy of bootstrap approximation of the MLE is investigated. Furthermore, it is shown that the MLE is asymptotically minimax efficient.