GSDAR: a fast Newton algorithm for $$\ell _0$$ ℓ 0 regularized generalized linear models with statistical guarantee

Jian Huang (), Yuling Jiao, Lican Kang, Jin Liu, Yanyan Liu () and Xiliang Lu
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Jian Huang: University of Iowa
Yuling Jiao: Wuhan University
Lican Kang: Wuhan University
Jin Liu: Center of Quantitative Medicine Duke-NUS Medical School
Yanyan Liu: Wuhan University
Xiliang Lu: Wuhan University

Computational Statistics, 2022, vol. 37, issue 1, No 22, 507-533

Abstract: Abstract We propose a fast Newton algorithm for $$\ell _0$$ ℓ 0 regularized high-dimensional generalized linear models based on support detection and root finding. We refer to the proposed method as GSDAR. GSDAR is developed based on the KKT conditions for $$\ell _0$$ ℓ 0 -penalized maximum likelihood estimators and generates a sequence of solutions of the KKT system iteratively. We show that GSDAR can be equivalently formulated as a generalized Newton algorithm. Under a restricted invertibility condition on the likelihood function and a sparsity condition on the regression coefficient, we establish an explicit upper bound on the estimation errors of the solution sequence generated by GSDAR in supremum norm and show that it achieves the optimal order in finite iterations with high probability. Moreover, we show that the oracle estimator can be recovered with high probability if the target signal is above the detectable level. These results directly concern the solution sequence generated from the GSDAR algorithm, instead of a theoretically defined global solution. We conduct simulations and real data analysis to illustrate the effectiveness of the proposed method.

Keywords: High-dimensional generalized linear models; Sparse learning; $$\ell _0$$ ℓ 0 -penalty; Support detection; Estimation error (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01098-z

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