Results 21 to 30 of about 3,667,438 (313)
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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Bootstrap-quantile ridge estimator for linear regression with applications
PLoS ONEBootstrap is a simple, yet powerful method of estimation based on the concept of random sampling with replacement. The ridge regression using a biasing parameter has become a viable alternative to the ordinary least square regression model for the ...
Irum Sajjad Dar, Sohail Chand
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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.
exaly +2 more sources
Robust, randomized preconditioning for kernel ridge regression [PDF]
arXiv.org, 2023 This paper investigates preconditioned conjugate gradient techniques for solving kernel ridge regression (KRR) problems with a medium to large number of data points ($10^4 \leq N \leq 10^7$), and it describes two methods with the strongest guarantees ...
M. D'iaz +4 more
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On the Optimality of Misspecified Kernel Ridge Regression [PDF]
International Conference on Machine Learning, 2023 In the misspecified kernel ridge regression problem, researchers usually assume the underground true function $f_{\rho}^{*} \in [\mathcal{H}]^{s}$, a less-smooth interpolation space of a reproducing kernel Hilbert space (RKHS) $\mathcal{H}$ for some $s ...
Haobo Zhang +3 more
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Multi-task learning on nuclear masses and separation energies with the kernel ridge regression [PDF]
Physics Letters B, 2022 A multi-task learning (MTL) framework, called gradient kernel ridge regression, for nuclear masses and separation energies is developed by introducing gradient kernel functions to the kernel ridge regression (KRR) approach.
X. Wu, Y.Y. Lu, P. Zhao
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Migration Letters, 2023 This study examines the way individuals in the United Arab Emirates handled their finances during the COVID-19 pandemic, with a focus on optimizing linear regression models utilizing Lasso and Ridge Regression methods.
S. Safi +5 more
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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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An elementary analysis of ridge regression with random design [PDF]
Comptes rendus. Mathematique, 2022 In this note, we provide an elementary analysis of the prediction error of ridge regression with random design. The proof is short and self-contained. In particular, it bypasses the use of Rudelson's deviation inequality for covariance matrices, through ...
Jaouad Mourtada, L. Rosasco
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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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