Results 1 to 10 of about 46,526 (254)
Feature-space selection with banded ridge regression [PDF]
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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Minimizing Stochastic Complexity with Ridge Regression [PDF]
We derive a penalty strength criterion for ridge regression using stochastic complexity, which is a refined variant of the minimum description length principle.
Antony Mizzi +2 more
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Ridge regression and its applications in genetic studies. [PDF]
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]
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.
Aamir Shahzad +3 more
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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]
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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Predictive efficiency of ridge regression estimator [PDF]
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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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 +6 more
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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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Correlation Based Ridge Parameters in Ridge Regression with Heteroscedastic Errors and Outliers [PDF]
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
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