Results 11 to 20 of about 46,526 (254)
Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. [PDF]
Gigascience, 2020 Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used
Rokem A, Kay K.
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Boosting ridge regression [PDF]
Computational Statistics & Data Analysis, 2007 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. In the direct approach the ridge estimator is used to fit iteratively the current residuals yielding an alternative
Gerhard Tutz 0001, Harald Binder
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An identity for kernel ridge regression [PDF]
Theoretical Computer Science, 2010 35 pages; extended version of ALT 2010 paper (Proceedings of ALT 2010, LNCS 6331, Springer, 2010)
Fedor Zhdanov, Yuri Kalnishkan
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Low-Rank Tensor Thresholding Ridge Regression
IEEE Access, 2019 In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information ...
Kailing Guo +3 more
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Treating Multicollinearity Problem Using Gool Programming Technique [PDF]
The Egyptian Statistical Journal, 2011 Multiple regression analysis is usually efficient for prediction, but often produces poor results because of the multicollinearity among the independent variables.
Afaf El-Dash +2 more
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Suggested Methods in Ridge Regression [PDF]
المجلة العراقية للعلوم الاحصائية, 2006 Three suggested procedures were adopted to determine the value of biasing parameter (k) in ridge regression: 1-fragmenting the ridge trace to groups each group contain semi-homogeneous absolute values of the estimated parameters, 2-rotating over the ...
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Logistic regression diagnostics in ridge regression
Computational Statistics, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M. Revan Özkale +2 more
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Mathematics, 2022 For the linear model Y=Xb+error, where the number of regressors (p) exceeds the number of observations (n), the Elastic Net (EN) was proposed, in 2005, to estimate b.
Rajaram Gana
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On Multivariate Ridge Regression [PDF]
Biometrika, 1987 A multivariate linear regression model with q responses as a linear function of p independent variables is considered with a \(p\times q\) parameter matrix B. The least-squares or normal-theory maximum likelihood estimate of B is deficient in that it takes no account of the `across regression' correlations, and ignores the Stein effect.
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Adaptive ridge regression for rare variant detection. [PDF]
PLoS ONE, 2012 It is widely believed that both common and rare variants contribute to the risks of common diseases or complex traits and the cumulative effects of multiple rare variants can explain a significant proportion of trait variances.
Haimao Zhan, Shizhong Xu
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