摘要
依据经验Bayes(EB)估计的思想方法,研究在LINEX损失函数下指数族刻度参数的EB估计问题.在这种损失函数下,求得参数的Bayes估计,利用密度函数的核估计方法,构造了总体X的密度函数估计,从而得到参数的EB估计,证明了这种EB估计是渐近最优的,并获得了它的收敛速度,最后将这种方法推广到多参数情形,并举例、模拟说明了它的应用.
Based on the experience of Bayes （EB） estimate of the way of thinking, research in the LINEX loss function scale parameter Exponential Family of EB estimation problem. In this loss function to obtain the Bayes paraIneter estimates, the use of nuclear density function estimation methods, construct a sample of density function estimates, resulting in estimated parameters of EB, EB to prove this estimate is asymptotically optimal, and its convergence rate, and finally this method will be extended to multi-parameter case, and an example illustrates its application.
出处
《数理统计与管理》
CSSCI
北大核心
2010年第6期1018-1025,共8页
Journal of Applied Statistics and Management
基金
江西省2008年教改资助项目(编号为JXJG-08-15-26)
关键词
指数族
刻度参数
经验BAYES估计
渐近最优
收敛速度
exponential family, calibration parameters, experience Bayes estimation, asymptotic optimal, the convergence rate
