Results 1 to 10 of about 52,512 (299)
New ridge parameters for ridge regression [PDF]
Journal of the Association of Arab Universities for Basic and Applied Sciences, 2014 AbstractHoerl and Kennard (1970a) introduced the ridge regression estimator as an alternative to the ordinary least squares (OLS) estimator in the presence of multicollinearity. In ridge regression, ridge parameter plays an important role in parameter estimation.
Dorugade, A.V.
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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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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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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 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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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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Kuwait Journal of Science, 2023 Ridge regression is employed to estimate the regression parameters while circumventing the multicollinearity among independent variables. The ridge parameter plays a vital role as it controls bias-variance tradeoff. Several methods for choosing the ridge
Irum Sajjad Dar +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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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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