Group selection and shrinkage: Structured sparsity for semiparametric models. (arXiv:2105.12081v2 [stat.ME] UPDATED)

Sparse regression and classification estimators that respect group structures
have application to an assortment of statistical and machine learning problems,
from multitask learning to sparse additive modeling to hierarchical selection.
This work introduces structured sparse estimators that combine group subset
selection with shrinkage. To accommodate sophisticated structures, our
estimators allow for arbitrary overlap between groups. We develop an
optimization framework for fitting the nonconvex regularization surface and
present finite-sample error bounds for estimation of the regression function.
As an application …

arxiv shrinkage sparsity