Regularization with Maximum Entropy and Quantum Electrodynamics: The Merg(E) Estimators
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Publication:3178520
DOI10.1080/03610918.2014.957838zbMath1382.81005OpenAlexW2015659490MaRDI QIDQ3178520
Pedro Macedo, Elvira Silva, Manuel G. Scotto
Publication date: 14 July 2016
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10773/15003
Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of statistics to physics (62P35) Electromagnetic interaction; quantum electrodynamics (81V10) Computational methods for problems pertaining to quantum theory (81-08)
Cites Work
- Unnamed Item
- Maximum entropy autoregressive conditional heteroskedasticity model
- Tsallis mutual information for document classification
- A weighted generalized maximum entropy estimator with a data-driven weight
- Bayesian methods and maximum entropy for ill-posed inverse problems
- Comparison of maximum entropy and higher-order entropy estimators.
- Comparative statics of the generalized maximum entropy estimator of the general linear model
- A generalized information theoretical approach to tomographic reconstruction
- On the Choice of the Ridge Parameter: A Maximum Entropy Approach
- Information Theory and Statistical Mechanics
- A General Class of Estimators for the Linear Regression Model Affected by Collinearity and Outliers
- On Some Ridge Regression Estimators: An Empirical Comparisons
- ROBUST RIDGE REGRESSION BASED ON AN M-ESTIMATOR
- A biased-robust regression technique for the combined outlier-multicollinearity problem
- Using Liu-Type Estimator to Combat Collinearity
- The Minimum Entropy Mapping Spectrum of a DNA Sequence
- Robust Statistics
- Imposing parameter inequality restrictions using the principle of maximum entropy
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