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Category: Methods and Measures

Description: Ridge and LASSO regression are alternative approaches in situations where a large number of predictor variables are available to choose from, and where a high degree of correlation among those predictor variables is possible. These methods guide selection of the most important predictor variables (even from among hundreds) without having to ‘fish’ for significance. The resulting models are parsimonious, with easy to interpret estimates, that also avoid problems with collinearity, biased estimates, and overfitting.