Iterative selection using orthogonal regression techniques

DOI10.1002/sam.11212OpenAlexW1521216656WikidataQ57424852 ScholiaQ57424852MaRDI QIDQ2870764

Subhashis Ghosal, Bradley C. Turnbull, Hao Helen Zhang

Publication date: 21 January 2014

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

Full work available at URL: https://doi.org/10.1002/sam.11212

zbMATH Keywords

orthogonalizationLASSOforward selectionhigh dimensional regression

Mathematics Subject Classification ID

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

Uses Software

• SSS

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• FIRST: combining forward iterative selection and shrinkage in high dimensional sparse linear regression
• Least angle regression. (With discussion)
• Spike and slab variable selection: frequentist and Bayesian strategies
• Boosting for high-dimensional linear models
• Forward Regression for Ultra-High Dimensional Variable Screening
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Shotgun Stochastic Search for “Largep” Regression
• Regularization and Variable Selection Via the Elastic Net
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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