A well‐known concern regarding the usual linear regression model is multicollinearity. As the strength of the association among the independent variables increases, the squared standard error of regression estimators tends to increase, which can seriously impact power. This paper examines heteroscedastic methods for dealing with this issue when testing the hypothesis that all of the slope parameters are equal to zero via a robust ridge estimator that guards against outliers among the dependent variable. Included are results related to leverage points, meaning outliers among the independent variables. In various situations, the proposed method increases power substantially.

中文翻译：

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稳健回归：当存在多重共线性或异方差时，测试关于斜率的整体假设。

关于通常的线性回归模型的一个众所周知的关注是多重共线性。随着自变量之间关联的强度增加，回归估计量的平方标准误差趋于增加，这会严重影响功效。本文通过一个稳健的岭估计器来检验所有斜率参数等于零的假设时，研究了异方差方法来处理此问题，该估计器可以防止因变量中的离群值。包括与杠杆点相关的结果，意味着自变量之间的离群值。在各种情况下，所提出的方法大大提高了功率。