A sequential test for variable selection in high dimensional complex data

DOI10.1016/j.csda.2014.07.016OpenAlexW2044541520MaRDI QIDQ1623732

Kofi P. Adragni, Moumita Karmakar

Publication date: 23 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.2014.07.016

zbMATH Keywords

dimension reductiondata analysishigh dimensionalityprincipal componentsvariable selectionvariable screeninginverse regression

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Linear regression; mixed models (62J05) Sequential statistical analysis (62L10)

Uses Software

• R
• glmnet
• spls
• ldr
• RSIR

Cites Work

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• Fisher lecture: Dimension reduction in regression
• Coordinate-independent sparse sufficient dimension reduction and variable selection
• Principal fitted components for dimension reduction in regression
• Testing predictor contributions in sufficient dimension reduction.
• Sparse sufficient dimension reduction using optimal scoring
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Better Subset Regression Using the Nonnegative Garrote
• Sufficient dimension reduction and prediction in regression
• On Directional Regression for Dimension Reduction
• Sliced Inverse Regression for Dimension Reduction
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
• De-noising by soft-thresholding
• Positive-Definite ℓ₁-Penalized Estimation of Large Covariance Matrices
• Regularization and Variable Selection Via the Elastic Net
• Sparse sufficient dimension reduction
• Sliced Inverse Regression with Regularizations

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