Tuning parameter calibration for $\ell_1$-regularized logistic regression

Wei Li; Johannes Lederer

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arXiv · 1610.00207

Tuning parameter calibration for $\ell_1$-regularized logistic regression

Wei Li·Johannes Lederer

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Abstract

Feature selection is a standard approach to understanding and modeling high-dimensional classification data, but the corresponding statistical methods hinge on tuning parameters that are difficult to calibrate. In particular, existing calibration schemes in the logistic regression framework lack any finite sample guarantees. In this paper, we introduce a novel calibration scheme for $\ell_1$-penalized logistic regression. It is based on simple tests along the tuning parameter path and is equipped with optimal guarantees for feature selection. It is also amenable to easy and efficient implementations, and it rivals or outmatches existing methods in simulations and real data applications.

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Wei Li, Johannes Lederer. 2016-10-01. Tuning parameter calibration for $\ell_1$-regularized logistic regression. https://arxiv.org/abs/1610.00207

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