Variable selection for sparse logistic regression

被引：3

作者：

Yin, Zanhua ^{[1
]}

机构：

[1] Gannan Normal Univ, Ganzhou, Peoples R China

来源：

METRIKA | 2020年 / 83卷 / 07期

关键词：

Score function; High dimensions; Lasso; Logistic regression model; Sparse; GENERALIZED LINEAR-MODELS; GROUP LASSO; ORACLE INEQUALITIES; REGULARIZATION; CLASSIFICATION;

D O I：

10.1007/s00184-020-00764-4

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We consider the variable selection problem in a sparse logistical regression model. Inspired by the square-root Lasso, we develop a weighted score Lasso for logistical regression. The new method yields the estimation l(1) error bound under similar assumptions as introduced in Bach et al. (Electron J Stat 4:384-414, 2010). Compared to standard Lasso, the weighted score Lasso provides a direct choice for the tuning parameter. Both theoretical and simulation results confirm the satisfactory performance of the proposed method. We illustrate our methodology with a real microarray data set.

引用

页码：821 / 836

页数：16

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