Number of predictors and multicollinearity: What are their effects on error and bias in regression?
Number of predictors and multicollinearity: What are their effects on error and bias in regression?
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DOI：
10.1080/03610918.2017.1371750
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发表时间：
2019-01-01
影响因子：
0.9
通讯作者：
Xu, Lihua
中科院分区：
文献类型：
--
作者：
Lavery, Matthew Ryan;Acharya, Parul;Xu, Lihua
目前的蒙特卡罗模拟研究通过分析参数偏差、类型I和类型II的错误率以及通过具有两个、四个和六个预测值的多个回归在各种多重共线性条件下产生的方差膨胀因子(VIF)值来补充文献。研究结果表明，多重共线性与第一类错误无关，但会增加第二类错误。对偏差的研究表明，多重共线性增加了参数偏差的可变性，同时导致了对参数的总体低估。共线也会增加VIF。然而，在所有诊断的情况下，增加预测器的数量与多重共线性相互作用，使观察到的问题复杂化。
The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate multicollinearity is unrelated to Type I error, but increases Type II error. Investigation of bias suggests that multicollinearity increases the variability in parameter bias, while leading to overall underestimation of parameters. Collinearity also increases VIF. In the case of all diagnostics however, increasing the number of predictors interacts with multicollinearity to compound observed problems.