多元统计
单变量
多元分析
统计
优势(遗传学)
回归分析
回归
数学
贝叶斯多元线性回归
一般化
相关性
计量经济学
生物
基因
几何学
数学分析
生物化学
作者
Razia Azen,David V. Budescu
标识
DOI:10.3102/10769986031002157
摘要
Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R 2 contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria for an appropriate generalization of R 2 to the case of multiple response variables. The DA results obtained by univariate regression (with each criterion separately) are analytically compared with results obtained by multivariate DA and illustrated with an example. It is shown that univariate dominance does not necessarily imply multivariate dominance (and vice versa), and it is recommended that researchers who wish to account for the correlation among the response variables use multivariate DA to determine the relative importance of predictors.
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