DOMINANCE ANALYSIS - A NEW APPROACH TO THE PROBLEM OF RELATIVE IMPORTANCE OF PREDICTORS IN MULTIPLE-REGRESSION
DOMINANCE ANALYSIS - A NEW APPROACH TO THE PROBLEM OF RELATIVE IMPORTANCE OF PREDICTORS IN MULTIPLE-REGRESSION
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DOI：
10.1037/0033-2909.114.3.542
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发表时间：
1993-11-01
影响因子：
22.4
通讯作者：
BUDESCU, DV
中科院分区：
文献类型：
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作者：
BUDESCU, DV
每当使用多元回归来测试和比较理论上驱动的模型时，确定预测变量的相对重要性是有意义的。具体来说，研究人员寻求根据变量的重要性对顺序和尺度变量进行排序，并将模型的全局统计数据表达为这些度量的函数。本文回顾了多元回归中预测变量重要性的多种含义，强调了它们的弱点，并提出了一种比较变量的新方法：显性分析。支配性是一种以成对方式定义的定性关系：如果一个变量在所有子集回归中比其竞争对手更有用，则称该变量支配另一个变量。描述并说明了新提出的方法的特性。
Whenever multiple regression is used to test and compare theoretically motivated models, it is of interest to determine the relative importance of the predictors. Specifically, researchers seek to rank order and scale variables in terms of their importance and to express global statistics of the model as a function of these measures. This article reviews the many meanings of importance of predictors in multiple regression, highlights their weaknesses, and proposes a new method for comparing variables: dominance analysis. Dominance is a qualitative relation defined in a pairwise fashion: One variable is said to dominate another if it is more useful than its competitor in all subset regressions. Properties of the newly proposed method are described and illustrated.