Results 271 to 280 of about 3,187,155 (299)

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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A Generalized Stochastic Restricted Ridge Regression Estimator

Communications in Statistics - Theory and Methods, 2014
In this article, we introduce a new stochastic restricted estimator for the unknown vector parameter in the linear regression model when stochastic linear restrictions on the parameters hold. We show that the new estimator is a generalization of the ordinary mixed estimator (OME), Liu estimator (LE), ordinary ridge estimator (ORR), (k-d) class ...
M. I. Alheety, B. M. Golam Kibria
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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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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 Üstündag Siray   +1 more
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Kernel ridge regression for general noise model with its application

Neurocomputing, 2015
The classical ridge regression technique makes an assumption that the noise is Gaussian. However, it is reported that the noise models in some practical applications do not satisfy Gaussian distribution, such as wind speed prediction. In this case, the classical regression techniques are not optimal.
Shiguang Zhang, Qinghua Hu, Zongxia Xie, Ju-Sheng Mi   +3 more
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Subset Selection in Linear Regression Using Generalized Ridge Estimator

Journal of Statistical Theory and Practice, 2010
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Dorugade, A. V., Kashid, D. N.
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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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Marginal maximum likelihood estimation methods for the tuning parameters of ridge, power ridge, and generalized ridge regression

Communications in Statistics - Simulation and Computation, 2017
This study introduces fast marginal maximum likelihood (MML) algorithms for estimating the tuning (shrinkage) parameter(s) of the ridge and power ridge regression models, and an automatic plug-in M...
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On generalized ridge regression estimators under collinearity and balanced loss

Applied Mathematics and Computation, 2002
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Minimum Message Length Ridge Regression for Generalized Linear Models

2013
This paper introduces an information theoretic model selection and ridge parameter estimation criterion for generalized linear models based on the minimum message length principle. The criterion is highly general in nature, and handles a range of target distributions, including the normal, binomial, Poisson, geometric and gamma distributions ...
Daniel F. Schmidt, Enes Makalic
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