Results 11 to 20 of about 3,667,438 (313)
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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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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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 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 ...
Rokem A, Kay K.
europepmc +7 more sources
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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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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Development of a novel modeling framework based on weighted kernel extreme learning machine and ridge regression for streamflow forecasting. [PDF]
Sci RepA precise streamflow forecast is crucial in hydrology for flood alerts, water quantity and quality management, and disaster preparedness. Machine learning (ML) techniques are commonly employed for hydrological prediction; however, they still face certain
Samadi-Koucheksaraee A, Chu X.
europepmc +2 more sources
Anomalies in the Foundations of Ridge Regression [PDF]
International Statistical Review, 2008 SummaryErrors persist in ridge regression, its foundations, and its usage, as set forth inHoerl & Kennard (1970)and elsewhere. Ridge estimators need not be minimizing, nor a prospective ridge parameter be admissible. Conventional estimators are not LaGrange's solutions constrained to fixed lengths, as claimed, since such solutions are singular.
Jensen, Donald R., Ramirez, Donald E.
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Random Design Analysis of Ridge Regression [PDF]
Foundations of Computational Mathematics, 2014 This work gives a simultaneous analysis of both the ordinary least squares estimator and the ridge regression estimator in the random design setting under mild assumptions on the covariate/response distributions. In particular, the analysis provides sharp results on the ``out-of-sample'' prediction error, as opposed to the ``in-sample'' (fixed design ...
Hsu, Daniel +2 more
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An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression. [PDF]
Mol Ther Nucleic Acids, 2020 In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines.
Liu C +8 more
europepmc +2 more sources