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Asymptotic theory for the MLE from randomly censored exponential samples

Asymptotic theory for the MLE from randomly censored exponential samples
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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. 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.
出处 《Chinese Science Bulletin》 SCIE EI CAS 1998年第13期1071-1076,共6页
基金 thePostdoctoralScienceFoundationofChina
关键词 maximum LIKELIHOOD estimate Edgeworth expansion BOOTSTRAP approximation asymptotic MINIMAX efficiency law of ITERATED logarithm. maximum likelihood estimate Edgeworth expanelon bootstrap approximation asymptotic minimax efficiency law of iterated logarithm
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