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Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
Divide and Conquer Kernel Ridge Regression A Distributed Algorithm Minimax Optimal Rates
2013/6/14
We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subse...
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
Ridge Regression dimensional subspace
2011/6/16
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which
one simply projects the data onto a finite dimensional subspace (as specified by a Principal
Component...
Ridge parameter for g-prior distribution in probit mixed models with collinearity
Ridge parameter for g probit mixed models with collinearity g-prior of Zellner
2011/3/18
In the Bayesian variable selection framework, a common prior distribution for the regression coefficients is the g-prior of Zellner (1986). However, there are two standard cases in which the associate...
Effect of W，LR，and LM Tests on the Performance of Preliminary Test Ridge Regression Estimators
Lagrangian multiplier likelihood ratio test preliminary test ridge regression risk superiority Wald test
2009/3/10
The preliminary test ridge regression estimators (PTRRE) based on the Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests are considered in this paper. Using risks, the regions of opt...
Anomalies in the Foundations of Ridge Regression
Constrained optimization incomplete use of LaGrange’s method nonsingular distributions alternative foundations
2010/4/27
Anomalies persist in the foundations of ridge regression as set forth in Hoerl
and Kennard (1970) and subsequently. Conventional ridge estimators and their properties
do not follow on constraining l...