Model selection procedure for high‐dimensional data

DOI10.1002/sam.10088OpenAlexW4233907665WikidataQ33758274 ScholiaQ33758274MaRDI QIDQ4969736

Xiaotong Shen, Yongli Zhang

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc2992390

zbMATH Keywords

model selectionpower marketinformation criterionRIClarge \(p\) but small \(n\)

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (7)

Identifying Latent Structures in Restricted Latent Class Models ⋮ Online updating of information based model selection in the big data setting ⋮ Model Selection of Generalized Estimating Equation With Divergent Model Size ⋮ Consistent tuning parameter selection in high-dimensional group-penalized regression ⋮ Forward-Backward Selection with Early Dropping ⋮ A study on tuning parameter selection for the high-dimensional lasso ⋮ Influence Diagnostics for High-Dimensional Lasso Regression

Uses Software

• ARC

Cites Work

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• Estimating the dimension of a model
• Least angle regression. (With discussion)
• The risk inflation criterion for multiple regression
• Model selection: a Lagrange optimization approach
• On the ``degrees of freedom of the lasso
• Calibration and empirical Bayes variable selection
• Extended Bayesian information criteria for model selection with large model spaces
• The Covariance Inflation Criterion for Adaptive Model Selection
• Adaptive Model Selection

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