Results 241 to 250 of about 56,202 (281)

Minimax Adaptive Generalized Ridge Regression Estimators

Journal of the American Statistical Association, 1978
Abstract 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 ...
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Subset Selection in Linear Regression Using Generalized Ridge Estimator

Journal of Statistical Theory and Practice, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dorugade, A. V., Kashid, D. N.
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Comparing ordinary ridge and generalized ridge regression results obtained using genetic algorithms for ridge parameter selection

Communications in Statistics - Simulation and Computation, 2020
Ridge regression is an alternative to the ordinary least squares method when multicollinearity presents among the regressor variables in multiple linear regression analysis.
Barnabe Ndabashinze, Gülesen Üstündağ Şiray   +1 more
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The existence theorem in general ridge regression

Statistics & Probability Letters, 1988
\textit{A. E. Hoerl} and \textit{R. W. Kennard} [Technometrics 12, 55-67 (1970; Zbl 0202.172)] state that, like the ordinary ridge estimator, the general ridge estimator is also better than the least squares estimator relative to a mean square error. The proof of this result is given in this note.
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An Explicit Solution for Generalized Ridge Regression

Technometrics, 1975
The 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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Generalized ridge estimation of a semiparametric regression model

Wuhan University Journal of Natural Sciences, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu, Hongchang, Rao, Shaolin
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A note on adaptive generalized ridge regression estimator

Statistics & Probability Letters, 1990
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Wang, Song-Gui, Chow, Shein-Chung
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Comment on a generalized stochastic restricted ridge regression estimator

Communications in Statistics - Theory and Methods, 2016
ABSTRACTIn this note, we make some comments about the paper of Alheety and Kibria (2014) and correct the wrongly proved Theorems in that paper.
Kaçiranlar S., Dawoud I.
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A Generalized Diagonal Ridge-type Estimator in Linear Regression

Communications in Statistics - Theory and Methods, 2014
This article introduces a general class of biased estimator, namely a generalized diagonal ridge-type (GDR) estimator, for the linear regression model when multicollinearity occurs. The estimator represents different kinds of biased estimators when different parameters are obtained.
Fei-Bao Liang, Yi-Xin Lan
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A Note on a Power Generalization of Ridge Regression

Technometrics, 1975
O*(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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