A stepwise regression algorithm for high-dimensional variable selection

DOI10.1080/00949655.2014.902460zbMath1457.62212OpenAlexW2031220322MaRDI QIDQ5220827

Tsuey-Hwa Hu, Jing-Shiang Hwang

Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2014.902460

zbMATH Keywords

high-dimensional dataLassogenome-wide association studyforward regressionminimum \(p\)-value

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (4)

Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria ⋮ Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm ⋮ Fast stepwise regression based on multidimensional indexes ⋮ Forward-Backward Selection with Early Dropping

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• Least angle regression. (With discussion)
• Forward Regression for Ultra-High Dimensional Variable Screening
• Extended Bayesian information criteria for model selection with large model spaces
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Sure Independence Screening for Ultrahigh Dimensional Feature Space

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