Using random subspace method for prediction and variable importance assessment in linear regression
type: Item
instance of: scholarly article
title: Using random subspace method for prediction and variable importance assessment in linear regression
zbMATH Open document ID: 1471.62139
author: Jan Mielniczuk
author: Paweł Teisseyre
published in: Computational Statistics and Data Analysis
publication date: 2018-11-08T00:00:00Z
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 6975420
zbMATH Keywords: random subspace method
zbMATH Keywords: high-dimensional model selection
zbMATH Keywords: prediction
zbMATH Keywords: variable importance
zbMATH Keywords: positive selection rate
zbMATH Keywords: false discovery rate
describes a project that uses: UCI-ml
describes a project that uses: ElemStatLearn
describes a project that uses: relaimpo
describes a project that uses: caret
MaRDI profile type: Publication
full work available at URL: j.csda.2012.09.018
OpenAlex ID: W2069802481
cites work: Random forests
cites work: Estimating Optimal Transformations for Multiple Regression and Correlation
cites work: Boosting for high-dimensional linear models
cites work: Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
cites work: Consistency of Bayesian procedures for variable selection
cites work: Sure Independence Screening for Ultrahigh Dimensional Feature Space
cites work: Q3543468
cites work: Q3903884
cites work: Linear Model Selection by Cross-Validation
cites work: Estimation in high-dimensional linear models with deterministic design matrices
cites work: Selection of the order of an autoregressive model by Akaike's information criterion
cites work: 10.1162/153244303322753733
cites work: Q4864293
cites work: On the Distributional Properties of Model Selection Criteria
cites work: Consistent Variable Selection in Linear Models
cites work: Regularization and Variable Selection Via the Elastic Net
DOI: 10.1016/J.CSDA.2012.09.018
description: scientific article; zbMATH DE number 6975420