Ridge Regression and Generalized Maximum Entropy: An improved version of the Ridge–GME parameter estimator
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Publication:4976544
DOI10.1080/03610918.2015.1096378zbMath1368.62206OpenAlexW2383561645MaRDI QIDQ4976544
Publication date: 31 July 2017
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2015.1096378
Related Items (6)
Ridge estimation in linear mixed measurement error models using generalized maximum entropy ⋮ Freedman’s Paradox: A Solution Based on Normalized Entropy ⋮ Ridge-GME estimation in linear mixed models ⋮ A Bayesian asymmetric logistic model of factors underlying team success in top‐level basketball in Spain ⋮ Transformation of variables and the condition number in ridge estimation ⋮ On regularization of generalized maximum entropy for linear models
Uses Software
Cites Work
- A new biased estimator based on ridge estimation
- Least angle regression. (With discussion)
- Comparative statics of the generalized maximum entropy estimator of the general linear model
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- A Monte Carlo Study of Recent Ridge Parameters
- On Some Ridge Regression Estimators: An Empirical Comparisons
- Comparing generalised maximum entropy and partial least squares methods for structural equation models
- Ridge regression:some simulations
- Probability Theory
- Choosing Ridge Parameter for Regression Problems
- Performance of Some New Ridge Regression Estimators
- Using Liu-Type Estimator to Combat Collinearity
- Regularization and Variable Selection Via the Elastic Net
- Characterization of Ridge Trace Behavior
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