Ridge Regularization: An Essential Concept in Data Science

DOI10.1080/00401706.2020.1791959MaRDI QIDQ6636560

Trevor Hastie

Publication date: 12 November 2024

Published in: Technometrics (Search for Journal in Brave)

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

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• Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares
• Computer Age Statistical Inference
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Reconciling modern machine-learning practice and the classical bias–variance trade-off
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Efficient quadratic regularization for expression arrays
• Random forests
• The Elements of Statistical Learning

Related Items (7)

High-dimensional regression coefficient estimation by nuclear norm plus \(l_1\) norm penalization ⋮ Screen then select: a strategy for correlated predictors in high-dimensional quantile regression ⋮ Regularized parametric survival modeling to improve risk prediction models ⋮ A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint ⋮ Can’t Ridge Regression Perform Variable Selection? ⋮ Comment: Regularization via Bayesian Penalty Mixing ⋮ Comment: Ridge Regression—Still Inspiring After 50 Years

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