Asymptotic theory for the MLE from randomly censored exponential samples

被引：0
|
作者：
Wang, QH [1 ]
机构：
[1] Peking Univ, Dept Probabil & Stat, Beijing 100871, Peoples R China
来源：
CHINESE SCIENCE BULLETIN | 1998年 / 43卷 / 13期
关键词：
maximum likelihood estimate; Edgeworth expansion; bootstrap approximation; asymptotic minimax efficiency; law of iterated logarithm;
D O I：
10.1007/BF02883075
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
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.
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页码：1071 / 1076
页数：6
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