Confidence Intervals for Sparse Penalized Regression with Random Designs.

ABSTRACT: With the abundance of large data, sparse penalized regression techniques are commonly used in data analysis due to the advantage of simultaneous variable selection and estimation. A number of convex as well as non-convex penalties have been proposed in the literature to achieve sparse estimates. Despite intense work in this area, how to perform valid inference for sparse penalized regression with a general penalty remains to be an active research problem. In this paper, by making use of state-of-the-art optimization tools in stochastic variational inequality theory, we propose a unified framework to construct confidence intervals for sparse penalized regression with a wide range of penalties, including convex and non-convex penalties. We study the inference for parameters under the populat

SUBMITTER: Yu G 

PROVIDER: S-EPMC7716883 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature