نتایج جستجو برای: ridge regression method

تعداد نتایج: 1900430  

2007
Gregory E. Fasshauer Jack G. Zhang

In this paper we focus on two methods for multivariate approximation problems with non-uniformly distributed noisy data. The new approach proposed here is an iterated approximate moving least-squares method. We compare our method to ridge regression which filters out noise by using a smoothing parameter. Our goal is to find an optimal number of iterations for the iterative method and an optimal...

2015
Jibo Wu Yasin Asar

Schaefer et al. [15] proposed a ridge logistic estimator in logistic regression model. In this paper a new estimator based on the ridge logistic estimator is introduced in logistic regression model and we call it as almost unbiased ridge logistic estimator. The performance of the new estimator over the ridge logistic estimator and the maximum likelihood estimator in scalar mean squared error cr...

2014
Brian McWilliams Christina Heinze Nicolai Meinshausen Gabriel Krummenacher Hastagiri P. Vanchinathan

We propose LOCO, a distributed algorithm which solves large-scale ridge regression. LOCO randomly assigns variables to different processing units which do not communicate. Important dependencies between variables are preserved using random projections which are cheap to compute. We show that LOCO has bounded approximation error compared to the exact ridge regression solution in the fixed design...

2011
Jianwen Xu Hu Yang JIANWEN XU HU YANG

In this paper, the preliminary test almost unbiased ridge estimators of the regression coefficients based on the conflicting Wald (W), Likelihood ratio (LR) and Lagrangian multiplier (LM) tests in a multiple regression model with multivariate Student-t errors are introduced when it is suspected that the regression coefficients may be restricted to a subspace. The bias and quadratic risks of the...

2017
Gabriel Montes-Rojas Antonio F. Galvao

We propose to model endogeneity bias using prior distributions of moment conditions. The estimator can be obtained both as a method-of-moments estimator and in a Ridge penalized regression framework. We show the estimator’s relation to a Bayesian estimator.

2012
Isamu Nagai

In the present study, we consider the selection of model selection criteria for multivariate ridge regression. There are several model selection criteria for selecting the ridge parameter in multivariate ridge regression, e.g., the Cp criterion and the modified Cp (MCp) criterion. We propose the generalized Cp (GCp) criterion, which includes Cp andMCp criteria as special cases. The GCp criterio...

2011
Peter Exterkate Patrick J.F. Groenen Christiaan Heij Dick van Dijk

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predictive regression model is based on a shrinkage estimator to avoid overfitting. We extend the kernel ...

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