On the asymptotic risk of ridge regression with many predictors

DOI10.1007/s13226-024-00646-9MaRDI QIDQ6623995

Prabir Burman, Krishnakumar Balasubramanian, Debashis Paul

Publication date: 24 October 2024

Published in: Indian Journal of Pure \& Applied Mathematics (Search for Journal in Brave)

zbMATH Keywords

ridge regressionrandom matrix theorymulticollinearitydouble asymptoticseigenvalue decay

Mathematics Subject Classification ID

Linear inference, regression (62Jxx) Multivariate analysis (62Hxx) Probability theory on algebraic and topological structures (60Bxx)

Cites Work

• Random design analysis of ridge regression
• Spectral analysis of large dimensional random matrices
• Minimax ridge regression estimation
• High-dimensional asymptotics of prediction: ridge regression and classification
• Linear models. Least squares and alternatives
• Surprises in high-dimensional ridgeless least squares interpolation
• Minimax Adaptive Generalized Ridge Regression Estimators
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Ridge regression and asymptotic minimax estimation over spheres of growing dimension

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