Overlapping group lasso for high-dimensional generalized linear models

Shengbin Zhou, Jingke Zhou and Bo Zhang

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 19, 4903-4917

Abstract: Structured sparsity has recently been a very popular technique to deal with the high-dimensional data. In this paper, we mainly focus on the theoretical problems for the overlapping group structure of generalized linear models (GLMs). Although the overlapping group lasso method for GLMs has been widely applied in some applications, the theoretical properties about it are still unknown. Under some general conditions, we presents the oracle inequalities for the estimation and prediction error of overlapping group Lasso method in the generalized linear model setting. Then, we apply these results to the so-called Logistic and Poisson regression models. It is shown that the results of the Lasso and group Lasso procedures for GLMs can be recovered by specifying the group structures in our proposed method. The effect of overlap and the performance of variable selection of our proposed method are both studied by numerical simulations. Finally, we apply our proposed method to two gene expression data sets: the p53 data and the lung cancer data.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:48:y:2019:i:19:p:4903-4917

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DOI: 10.1080/03610926.2018.1500604

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