熵损失下一类广义指数分布刻度参数的经验Bayes估计（英文）  

Empirical Bayes Estimation of the Scale Parameter for a Generalized Exponential Distribution Under Entropy Loss

作　　者：王超[1] 陈家清[1] 刘次华[2] 

机构地区：[1]武汉理工大学理学院统计系,湖北武汉430070 [2]华中科技大学数学与统计学院,湖北武汉430074

出　　处：《应用数学》2016年第3期584-591,共8页Mathematica Applicata

基　　金：Supported by the Natural Science Fund of Hubei Province(2014CFB863);the Foundamental Research Funds for the Central Universities of China(2014-Ia-040)

摘　　要：本文在熵损失函数下获得一类广义指数分布刻度参数的贝叶斯估计,并构造了相应的经验贝叶斯估计.在适当的条件下证明所提出的经验贝叶斯估计的渐近最优性并获得了其收敛速度.最后,给出一个有关本文主要结果的例子.In this study, the Bayes estimator is derived under entropy loss function and empirical Bayes（EB） estimators of the scale parameter are constructed for a generalized exponential distribution. It is shown that the proposed EB estimators are asymptotically optimal and obtained its convergence rates under suitable conditions. Finally, an example about the main results of this paper is given.

关 键 词：经验贝叶斯估计 广义指数分布 熵损失 收敛速度 

分 类 号：O212.1[理学—概率论与数理统计]