Penalized Maximum Likelihood Principle for Choosing Ridge Parameter

DOI10.1080/03610910903061014zbMath1191.62125OpenAlexW1976381939MaRDI QIDQ3652709

Minh-Ngoc Tran

Publication date: 16 December 2009

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910903061014

zbMATH Keywords

model selectionridge regressionridge parameterpenalized MLdata-dependent penaltyloss rank principle

Mathematics Subject Classification ID

Related Items (5)

Computational Method for Jackknifed Generalized Ridge Tuning Parameter based on Generalized Maximum Entropy ⋮ The Loss Rank Criterion for Variable Selection in Linear Regression Analysis ⋮ Efficient approximate k‐fold and leave‐one‐out cross‐validation for ridge regression ⋮ On the Choice of the Ridge Parameter: A Maximum Entropy Approach ⋮ Bias-correction for Weibull common shape estimation

Cites Work

• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Some Modifications for Choosing Ridge Parameters
• A Monte Carlo Study of Recent Ridge Parameters
• Choosing Ridge Parameter for Regression Problems
• The Loss Rank Principle for Model Selection
• Tuning parameter selectors for the smoothly clipped absolute deviation method
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