Results 11 to 20 of about 178,006 (285)

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 ...
Dupré la Tour T, Eickenberg M, Nunez-Elizalde AO, Gallant JL.   +3 more
europepmc   +2 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.
Arashi M, Roozbeh M, Hamzah NA, Gasparini M.   +3 more
europepmc   +2 more sources

New ridge parameter estimators for the quasi-Poisson ridge regression model. [PDF]

Sci Rep
The 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.
Shahzad A, Amin M, Emam W, Faisal M.
europepmc   +2 more sources

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.
Takeuchi F, Kato N.
europepmc   +2 more sources

Optimum Ridge Regression Parameter Using R-Squared of Prediction as a Criterion for Regression Analysis

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
doaj   +1 more source

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
doaj   +1 more source

Initial Coin Offering Prediction Comparison Using Ridge Regression, Artificial Neural Network, Random Forest Regression, and Hybrid ANN-Ridge

Mendel, 2023
Can machine learning take a prediction to win an investment in ICO (Initial Coin Offering)? In this research work, our objective is to answer this question. Four popular and lower computational demanding approaches including Ridge regression (RR),
Toai Kim Tran, Roman Senkerik, Hahn Thi Xuan Vo, Huan Minh Vo, Adam Ulrich, Marek Musil, Ivan Zelinka   +6 more
doaj   +1 more source

Correlation Based Ridge Parameters in Ridge Regression with Heteroscedastic Errors and Outliers [PDF]

Journal of Statistical Theory and Applications (JSTA), 2015
This paper introduces some new estimators for estimating ridge parameter, based on correlation between response and regressor variables for ridge regression analysis.
A.V. Dorugade
doaj   +1 more source

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, Tong Zhang, Xiangmin Xu, Xiaofen Xing   +3 more
doaj   +1 more source

Ridge Regression and the Elastic Net: How Do They Do as Finders of True Regressors and Their Coefficients?

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
doaj   +1 more source