Random subspace method for high-dimensional regression with the \texttt{R} package \texttt{regRSM}

DOI10.1007/s00180-016-0658-2zbMath1347.65033OpenAlexW2470652322WikidataQ59473865 ScholiaQ59473865MaRDI QIDQ311298

Robert A. Kłopotek, Paweł Teisseyre, Jan Mielniczuk

Publication date: 29 September 2016

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-016-0658-2

zbMATH Keywords

MPIhigh-dimensional regression\texttt{R}generalized information criterionrandom subspace methodvariable importance measure

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Software, source code, etc. for problems pertaining to statistics (62-04) Linear regression; mixed models (62J05)

Related Items (2)

fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data ⋮ A note on variable selection in functional regression via random subspace method

Uses Software

• R
• UCI-ml
• caret
• foreach
• Rmpi
• glmnet
• relaimpo
• ElemStatLearn
• doParallel
• regRSM
• care

Cites Work

• High-Dimensional Regression and Variable Selection Using CAR Scores
• Estimation in high-dimensional linear models with deterministic design matrices
• Multivariate adaptive regression splines
• Using random subspace method for prediction and variable importance assessment in linear regression
• Extended Bayesian information criteria for model selection with large model spaces
• A sparse ising model with covariates
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Regularization Parameter Selections via Generalized Information Criterion
• Regularization and Variable Selection Via the Elastic Net
• Linear Models in Statistics
• Tuning Parameter Selection in High Dimensional Penalized Likelihood
• Matrix Algebra
• Random forests
• A general theory of concave regularization for high-dimensional sparse estimation problems
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