Simultaneous dimension reduction and variable selection in modeling high dimensional data

DOI10.1016/j.csda.2017.03.015zbMath1464.62113OpenAlexW2597996432MaRDI QIDQ1654282

Joseph Ryan G. Lansangan, Erniel B. Barrios

Publication date: 7 August 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.2017.03.015

zbMATH Keywords

dimension reductionhigh dimensionalityvariable selectionsparsitylatent factorssparse principal component analysissoft thresholdingregression modeling

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)

Related Items (2)

Dimension reduction based on conditional multiple index density function ⋮ Sure independence screening in the presence of missing data

Uses Software

• BayesTree
• AS 223
• BartPy

Cites Work

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• BART: Bayesian additive regression trees
• Rejoinder: Fisher lecture: Dimension reduction in regression
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• Inference in High Dimensional Generalized Linear Models Based on Soft Thresholding
• Interpretable dimension reduction
• A Monte Carlo Evaluation of Some Ridge-Type Estimators
• Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
• Algorithm AS 223: Optimum Ridge Parameter Selection
• Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection
• Regularization and Variable Selection Via the Elastic Net
• Regression Analysis by Example
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Factor analysis regression

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