ARTICLE | doi:10.20944/preprints202311.1114.v1
Subject: Computer Science And Mathematics, Probability And Statistics Keywords: high-dimensional data; non-asymptotic inequality; logistic regression; variable selection; block coordinate descent algorithm
Online: 16 November 2023 (15:30:02 CET)
We present a new penalized method for estimation in sparse logistic regression models with group structure. Group sparsity suggests us to consider the Group Lasso penalty. Being different from penalized log-likelihood estimation, our method can be viewed as a penalized weighted score function method. Under some mild conditions, we provide non-asymptotic oracle inequalities promoting group sparsity of predictors. A modified block coordinate descent algorithm based on a weighted score function is also employed. The net advantage of our algorithm over the existing Group Lasso-type procedures is that the tuning parameter can be pre-specified. The simulations show that this algorithm is considerably faster and more stable than competing methods. Finally, we illustrate our methodology with two real data sets.