A0188
Title: LASSO-type penalization in the framework of generalized additive models for location, scale and shape (GAMLSS)
Authors: Andreas Groll - Technical University Dortmund (Germany) [presenting]
Abstract: A regularization approach is proposed for high dimensional data set-ups in the generalized additive model for location, scale and shape (GAMLSS) framework. It is designed for linear covariate effects and is based on L1-type penalties. The following three penalization options are provided: The conventional least absolute shrinkage and selection operator (LASSO) for metric covariates and both group and fused LASSO for categorical predictors.
