Principles of multilevel modelling
Principles of multilevel modelling
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DOI：
10.1093/ije/29.1.158
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发表时间：
2000-02-01
影响因子：
7.7
通讯作者：
Greenland, S
中科院分区：
文献类型：
--
作者：
Greenland, S
背景多层建模，也称为分层回归，概括了普通回归建模，以区分模型中的多层信息。使用多个级别可带来大量统计效益。为了帮助理解这些好处，本文提供了一个基本的介绍多级建模的概念基础，从经典的频率论，贝叶斯，贝叶斯技术作为特殊情况。本文重点介绍了多级平均（“收缩”）在减少估计误差中的作用，以及先验信息在寻找良好平均值中的作用。
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.