Principles of multilevel modelling

被引：338

作者：

Greenland, S ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Sch Publ Hlth, Dept Epidemiol, Topanga, CA 90290 USA

来源：

INTERNATIONAL JOURNAL OF EPIDEMIOLOGY | 2000年 / 29卷 / 01期

关键词：

Bayesian statistics; empirical-Bayes estimation; hierarchical regression; mixed models; multilevel modelling; random-coefficient regression; ridge regression; risk assessment; Stein estimation;

D O I：

10.1093/ije/29.1.158

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

Background Multilevel modelling, also known as hierarchical regression, generalizes ordinary regression modelling to distinguish multiple levels of information in a model. Use of multiple levels gives rise to an enormous range of statistical benefits. To aid in understanding these benefits, this article provides an elementary introduction to the conceptual basis for multilevel modelling, beginning with classical frequentist, Bayes, and empirical-Bayes techniques as special cases. The article focuses on the role of multilevel averaging ('shrinkage') in the reduction of estimation error, and the role of prior information in finding good averages.

引用

页码：158 / 167

页数：10

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