Efficient regularized regression with \(L_0\) penalty for variable selection and network construction

DOI10.1155/2016/3456153zbMath1367.92008OpenAlexW2532146222WikidataQ37397132 ScholiaQ37397132MaRDI QIDQ2011726

Gang Li, Zhenqiu Liu

Publication date: 4 August 2017

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/3456153

zbMATH Keywords

EM algorithms\(L_0\) regularized regressionhigh-dimensional big data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15) General nonlinear regression (62J02)

Related Items

A scalable surrogate \(L_0\) sparse regression method for generalized linear models with applications to large scale data ⋮ Simultaneous estimation and variable selection for incomplete event history studies ⋮ Simultaneous variable selection and estimation for joint models of longitudinal and failure time data with interval censoring ⋮ Variables selection using \(\mathcal{L}_0\) penalty ⋮ Simultaneous Estimation and Variable Selection for Interval-Censored Data With Broken Adaptive Ridge Regression ⋮ Broken adaptive ridge regression and its asymptotic properties

Uses Software

• sparsenet

Cites Work

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