Results 11 to 20 of about 3,187,155 (299)
Nonlinear Generalized Ridge Regression [PDF]
, 2023 A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relationship between a y-outcome variable and each of p = 2 or more potential x-predictor variables. The y-outcome is then predicted from all p of these "linearized" spline-predictors using the form of Generalized Ridge Regression that is most likely to yield ...
Obenchain, Robert L.
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Nonparametric Generalized Ridge Regression [PDF]
, 2023 The title of this paper is potentially misleading, its methods are neither innovative nor easy to apply, and its numerical example is nearly ...
Obenchain, Robert L.
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EM algorithm for generalized Ridge regression with spatial covariates [PDF]
EnvironmetricsAbstract The generalized Ridge penalty is a powerful tool for dealing with multicollinearity and high‐dimensionality in regression problems. The generalized Ridge regression can be derived as the mean of a posterior distribution with a Normal prior and a given covariance matrix.
Monbet, Valérie +3 more
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The feasible generalized restricted ridge regression estimator
Journal of Statistical Computation and Simulation, 2016 ABSTRACTThe presence of autocorrelation in errors and multicollinearity among the regressors have undesirable effects on the least-squares regression. There are a wide range of methods which are proposed to overcome the usefulness of the ordinary least-squares estimator or the generalized least-squares estimator, such as the Stein-rule, restricted ...
Özbay N., Kaçıranlar S., Dawoud I.
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A novel generalized ridge regression method for quantitative genetics. [PDF]
Genetics, 2013 AbstractAs the molecular marker density grows, there is a strong need in both genome-wide association studies and genomic selection to fit models with a large number of parameters. Here we present a computationally efficient generalized ridge regression (RR) algorithm for situations in which the number of parameters largely exceeds the number of ...
Shen X, Alam M, Fikse F, Rönnegård L.
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Generalized Ridge Regression: Biased Estimation for Multiple Linear Regression Models [PDF]
23 pages, 5 tables, 7 figures, working ...
Gómez, Román Salmerón +2 more
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The General Linear Test in the Ridge Regression [PDF]
Communications for Statistical Applications and Methods, 2014 We derive a test statistic for the general linear test in the ridge regression model. The exact distribution for the test statistic is too difficult to derive; therefore, we suggest an approximate reference distribution. We use numerical studies to verify that the suggested distribution for the test statistic is appropriate. A asymptotic result for the
Whasoo Bae, Minji Kim, Choongrak Kim
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Efficient Generalized Ridge Regression
Open Statistics, 2022 Abstract The original ridge estimator of the unknown p×1 vector of β-coefficients in a linear model used a single scalar, k, to determine a point on a shrinkage path of finite length that extends from the Ordinary Least Squares estimator, ^β 0, to the shrinkage terminus (usually ^β ≡ 0). Generalized ridge estimators use
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Boosting Ridge Regression [PDF]
, 2005 Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining ridge regression with boosting techniques.
Binder, Harald, Tutz, Gerhard
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Comparison of regression models under multi-collinearity [PDF]
, 2018 Multicollinearity is a major problem in linear regression analysis and several methods exists in the literature to deal with the same. Ridge regression is one of the most popular methods to overcome the problem followed by Generalized Ridge Regression ...
Srinivasan, Rangasami M.; Department of Statistics, University of Madras +2 more
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