Using random subspace method for prediction and variable importance assessment in linear regression

DOI10.1016/J.CSDA.2012.09.018zbMath1471.62139OpenAlexW2069802481MaRDI QIDQ1621353

Jan Mielniczuk, Paweł Teisseyre

Publication date: 8 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2012.09.018

zbMATH Keywords

predictionfalse discovery raterandom subspace methodvariable importancehigh-dimensional model selectionpositive selection rate

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05)

Related Items (6)

A high-dimensional two-sample test for the mean using random subspaces ⋮ Random subspace method for high-dimensional regression with the \texttt{R} package \texttt{regRSM} ⋮ A random subset implementation of weighted quantile sum (WQS_RS) regression for analysis of high-dimensional mixtures ⋮ Variable selection by searching for good subsets ⋮ Variable selection in functional regression models: a review ⋮ A note on variable selection in functional regression via random subspace method

Uses Software

• UCI-ml
• caret
• relaimpo
• ElemStatLearn

Cites Work

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• Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
• Estimation in high-dimensional linear models with deterministic design matrices
• Consistency of Bayesian procedures for variable selection
• Boosting for high-dimensional linear models
• Estimating Optimal Transformations for Multiple Regression and Correlation
• On the Distributional Properties of Model Selection Criteria
• Selection of the order of an autoregressive model by Akaike's information criterion
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• 10.1162/153244303322753733
• Consistent Variable Selection in Linear Models
• Linear Model Selection by Cross-Validation
• Regularization and Variable Selection Via the Elastic Net
• Random forests

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