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Feature-space selection with banded ridge regression [PDF]
NeuroImage, 2022 Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain ...
Tom Dupré la Tour +3 more
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Boosting Ridge Regression [PDF]
Computational Statistics & Data Analysis, 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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Ridge regression and its applications in genetic studies. [PDF]
PLoS ONE, 2021 With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling.
M Arashi +3 more
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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.
europepmc +5 more sources
Ridge regression revisited [PDF]
Statistica Neerlandica, 2005 We argue in this paper that general ridge (GR) regression implies no major complication compared with simple ridge regression. We introduce a generalization of an explicit GR estimator derived by Hemmerle and by Teekens and de Boer and show that this ...
Boer, P.M.C. (Paul) de +1 more
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New ridge parameter estimators for the quasi-Poisson ridge regression model [PDF]
Scientific ReportsThe quasi-Poisson regression model is used for count data and is preferred over the Poisson regression model in the case of over-dispersed count data.
Aamir Shahzad +3 more
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Nonlinear ridge regression improves cell-type-specific differential expression analysis [PDF]
BMC Bioinformatics, 2021 Background Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types.
Fumihiko Takeuchi, Norihiro Kato
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A Poisson Ridge Regression Estimator [PDF]
Economic Modelling, 2011 The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML). The ML method is very sensitive to multicollinearity. Therefore, we present a new Poisson ridge regression
Månsson, Kristofer, Shukur, Ghazi
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Journal of Statistical Theory and Applications (JSTA), 2021 The presence of the multicollinearity problem in the predictor data causes the variance of the ordinary linear regression coefficients to be increased so that the prediction power of the model not to be satisfied and sometimes unacceptable results be ...
Akbar Irandoukht
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Predictive efficiency of ridge regression estimator [PDF]
Yugoslav Journal of Operations Research, 2017 In this article we have considered the problem of prediction within and outside the sample for actual and average values of the study variables in case of ordinary least squares and ridge regression estimators.
Tiwari Manoj, Sharma Amit
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