Model Selection for High-Dimensional Quadratic Regression via Regularization
Model Selection for High-Dimensional Quadratic Regression via Regularization
Quadratic regression (QR) models naturally extend linear models by considering interaction effects between the covariates. To conduct model selection in QR, it is important to maintain the hierarchical model structure between main effects and interaction effects. Existing regularization methods generally achieve this goal by solving complex optimization problems, which usually …