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首页> 外文期刊>Journal of applied statistics >Robust regression: an inferential method for determining which independent variables are most important
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Robust regression: an inferential method for determining which independent variables are most important

机译：稳健回归：一种推论方法，用于确定哪些自变量最为重要

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摘要

Consider the usual linear regression model consisting of two or more explanatory variables. There are many methods aimed at indicating the relative importance of the explanatory variables. But in general these methods do not address a fundamental issue: when all of the explanatory variables are included in the model, how strong is the empirical evidence that the first explanatory variable is more or less important than the second explanatory variable? How strong is the empirical evidence that the first two explanatory variables are more important than the third explanatory variable? The paper suggests a robust method for dealing with these issues. The proposed technique is based on a particular version of explanatory power used in conjunction with a modification of the basic percentile method.
机译：考虑由两个或多个解释变量组成的常规线性回归模型。有许多方法旨在指示解释变量的相对重要性。但是总的来说，这些方法并没有解决一个基本问题：当所有解释变量都包含在模型中时，经验证据证明第一个解释变量比第二个解释变量重要多少？前两个解释变量比第三个解释变量重要的经验证据有多强？本文提出了一种解决这些问题的可靠方法。所提出的技术基于与基本百分位数方法的修改结合使用的解释能力的特定版本。

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