New method detects data overlap in regression models, improving accuracy.
The article explores ways to detect multicollinearity in logistic regression by using geometric interpretations of condition number and variance inflation factor (VIF) from linear regression. The researchers developed new measures for evaluating multicollinearity in generalized linear models, making it easier to identify when predictor variables are highly correlated.
