DERIVING GENERALIZED MEANS AS LEAST-SQUARES AND MAXIMUM-LIKELIHOOD-ESTIMATES

被引：12

作者：

BERGER, RL ^{[1
]}

CASELLA, G ^{[1
]}

机构：

[1] CORNELL UNIV,BIOMETR UNIT,ITHACA,NY 14853

来源：

AMERICAN STATISTICIAN | 1992年 / 46卷 / 04期

关键词：

ARITHMETIC MEAN; EXPONENTIAL FAMILY; GEOMETRIC MEAN; HARMONIC MEAN;

D O I：

10.2307/2685312

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Functions called generalized means are of interest in statistics because they are simple to compute, have intuitive appeal, and can serve as reasonable parameter estimates. The well-known arithmetic, geometric, and harmonic means are all examples of generalized means. We show how generalized means can be derived in a unified way, as least squares estimates for a transformed data set. We also investigate models that have generalized means as their maximum likelihood estimates.

引用

页码：279 / 282

页数：4