Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive Models
分组回归与稀疏广义加性模型的尖峰—厚尾组 Lasso
–We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse generalized additive models (GAMs), thereby introducing the first nonparametric variant of the spike-and-slab lasso methodology. Our model simultaneously performs group selection and estimation, while our fully Bayes treatment of the mixture proportion allows for model complexity control and…