Collinearity diagnostics of binary logistic regression model
Collinearity diagnostics of binary logistic regression model
复制标题
DOI：
10.1080/09720502.2010.10700699
复制
发表时间：
2010-01-01
影响因子：
1.7
通讯作者：
Rana, Sohel
中科院分区：
文献类型：
--
作者：
Midi, Habshah;Sarkar, S. K.;Rana, Sohel
多重共线性是一种统计现象，其中逻辑回归模型中的预测变量高度相关。当模型中存在大量协变量时，这种情况并不少见。多重共线性一直是统计建模中的千磅怪物。驯服这个怪物已被证明是统计建模研究的巨大挑战之一。多重共线性可能导致不稳定的估计和不准确的方差，影响置信区间和假设检验。共线性的存在扩大了参数估计值的方差，从而导致对解释变量和响应变量之间关系的不正确推断。检查相关矩阵可能有助于检测多重共线性，但还不够。更好的诊断是通过线性回归与选项公差，Vif，条件指数和方差比例。对于中到大样本量，删除一个相关变量的方法完全可以降低多重共线性。根据不同的共线性诊断，我们可以得出结论，在不增加样本量的情况下，第二种选择省略一个相关变量可以在很大程度上减少多重共线性。
Multicollinearity is a statistical phenomenon in which predictor variables in a logistic regression model are highly correlated. It is not uncommon when there are a large number of covariates in the model. Multicollinearity has been the thousand pounds monster in statistical modeling. Taming this monster has proven to be one of the great challenges of statistical modeling research. Multicollinearity can cause unstable estimates and inaccurate variances which affects confidence intervals and hypothesis tests. The existence of collinearity inflates the variances of the parameter estimates, and consequently incorrect inferences about relationships between explanatory and response variables. Examining the correlation matrix may be helpful to detect multicollinearity but not sufficient. Much better diagnostics are produced by linear regression with the option tolerance, Vif, condition indices and variance proportions. For moderate to large sample sizes, the approach to drop one of the correlated variables was established entirely satisfactory to reduce multicollinearity. On the light of different collinearity diagnostics, we may safely conclude that without increasing sample size, the second choice to omit one of the correlated variables can reduce multicollinearity to a great extent.