BAYESIAN PREDICTION IN LINEAR-MODELS - APPLICATIONS TO SMALL AREA ESTIMATION

被引：66

作者：

DATTA, GS

GHOSH, M

机构：

[1] UNIV FLORIDA,DEPT STAT,GAINESVILLE,FL 32611

[2] US BUR CENSUS,WASHINGTON,DC 20233

[3] US BUR LABOR STAT,WASHINGTON,DC 20214

来源：

ANNALS OF STATISTICS | 1991年 / 19卷 / 04期

关键词：

HIERARCHICAL BAYES; EMPIRICAL BAYES; MIXED LINEAR MODELS; BEST LINEAR UNBIASED PREDICTION; BEST UNBIASED PREDICTION; SMALL AREA ESTIMATION; NESTED ERROR REGRESSION MODEL; RANDOM REGRESSION COEFFICIENTS MODEL; 2-STAGE SAMPLING; ELLIPTICALLY SYMMETRICAL DISTRIBUTIONS;

D O I：

10.1214/aos/1176348369

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：

This paper introduces a hierarchical Bayes (HB) approach for prediction in general mixed linear models. The results find application in small area estimation. Our model unifies and extend, a number of models previously considered in this area. Computational formulas for obtaining the Bayes predictors and their standard errors are given in the general case. The methods are applied to two actual data sets. Also, in a special case, the HB predictors are shown to possess some interesting frequentist properties.

引用

页码：1748 / 1770

页数：23

共 34 条

[1] COMPUTATIONAL METHODS USING A BAYESIAN HIERARCHICAL GENERALIZED LINEAR-MODEL [J].