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Kernel Ridge Regression for Generalized Graph Signal Processing
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023National Research Foundation (NRF)
Jian, Xingchao, Tay, Wee Peng
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Minimax Adaptive Generalized Ridge Regression Estimators
Journal of the American Statistical Association, 1978Abstract We consider the problem of estimating the vector of regression coefficients of a linear model using generalized ridge regression estimators where the ridge constant is chosen on the basis of the data. For general quadratic loss we produce such estimators whose risk function dominates that of the least squares procedure provided the number of ...
William E. Strawderman
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An Explicit Solution for Generalized Ridge Regression
Technometrics, 1975The general form of ridge regression proposed by Hoerl and Kennard is examined in the context of the iterative procedure they suggest for obtaining optimal estimators. It is shown that a non-iterative, closed form solution is available for this procedure. The solution is found to depend upon certain convergence/divergence conditions which relate to the
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2020
In a generalized ridge (GR) regression, since a GR estimator (GRE) depends on ridge parameters, it is important to select those parameters appropriately. Ridge parameters selected by minimizing the generalized \(C_p\) (\(GC_p\)) criterion can be obtained as closed forms.
Mineaki Ohishi +2 more
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In a generalized ridge (GR) regression, since a GR estimator (GRE) depends on ridge parameters, it is important to select those parameters appropriately. Ridge parameters selected by minimizing the generalized \(C_p\) (\(GC_p\)) criterion can be obtained as closed forms.
Mineaki Ohishi +2 more
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1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019), 2003
Weighted linear regression (WLR) is computationally efficient for generating parametric images in dynamic PET studies. However, due to high noise level of pixel kinetics, parametric images estimated by WLR usually have high variability. The authors have shown earlier that, for image-wise model fitting, the incorporation of simple ridge regression and ...
null Yun Zhou +2 more
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Weighted linear regression (WLR) is computationally efficient for generating parametric images in dynamic PET studies. However, due to high noise level of pixel kinetics, parametric images estimated by WLR usually have high variability. The authors have shown earlier that, for image-wise model fitting, the incorporation of simple ridge regression and ...
null Yun Zhou +2 more
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Generalized ridge regression: a note on negative ridge parameters
Communications in Statistics - Theory and Methods, 1983Generalized ridge regression is extended to include negative values of the ridge parameter. In doing so, classes of biased estimators and estimators obtained by variable selection proce¬dures can be formulated as ridge estimators having stochastic, possibly negative, ridge parameter values.
Tsushung A. Hua, Richard F. Gunst
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A Note on a Power Generalization of Ridge Regression
Technometrics, 1975O*(k, q) = [X'X + k(X'X)-q]-'X'y = [X'X + Q'DQ]-'X'y (1.3) where D = diag (d,d2 * dp), di = k/X\i. Thus, ki = k/X,i in the general formulation of (1.1) and (1.2). In Hoerl and Kennard [1] it was shown that the optimum K matrix has elements ki = a2/ai2.
Arthur E. Hoerl, Robert W. Kennard
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On ordinary ridge regression in generalized linear models
Communications in Statistics - Theory and Methods, 1992In this paper it is shown that an ill-conditioned data matrix has similar effects on the parameter estimator when estimating generalized linear models as when estimating linear regression models.
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An Accelerated Maximally Split ADMM for a Class of Generalized Ridge Regression
IEEE Transactions on Neural Networks and Learning Systems, 2023Ridge regression (RR) has been commonly used in machine learning, but is facing computational challenges in big data applications. To meet the challenges, this article develops a highly parallel new algorithm, i.e., an accelerated maximally split alternating direction method of multipliers (A-MS-ADMM), for a class of generalized RR (GRR) that allows ...
Xiaoping Lai +2 more
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Generalized ridge estimation of a semiparametric regression model
Wuhan University Journal of Natural Sciences, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu, Hongchang, Rao, Shaolin
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