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Multivariate Regression Shrinkage and Selection by Canonical Correlation Analysis
Adaptive Lasso Canonical Correlation Analysis Multivariate Regression
2016/1/25
The problem of regression shrinkage and selection for multivariate regression is considered. The goal is to consistently identify those variables relevant for regression. This is done not only for pre...
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for ...
A pseudo-RIP for multivariate regression
Multivariate regression Restricted Isometry Property
2011/7/6
We give a suitable RI-Property under which recent results for trace regression translate into strong risk bounds for multivariate regression. This pseudo-RIP is compatible with the setting $n < p$.
Recursive bias estimation for multivariate regression smoothers
nonparametric regression；smoother；kernel；thin-plate splines；stopping rules
2011/6/17
This paper presents a practical and simple fully nonparametric multivariate smooth-
ing procedure that adapts to the underlying smoothness of the true regression function. Our
estimator is easily co...
Union support recovery in high-dimensional multivariate regression
Union support recovery high-dimensional multivariate regression
2010/4/30
In the problem of multivariate regression, a K-dimensional response vector is regressed
upon a common set of p covariates, with a matrix B 2 RpK of regression
coecients. We study the behavior of ...
Large and moderate deviations principles for kernel estimators of the multivariate regression
Nadaraya-Watson estimator Recursive kernel estimator Large deviations principle Moderatedeviations principle
2010/4/27
In this paper, we prove large deviations principle for the
Nadaraya-Watson estimator and for the semi-recursive kernel
estimator of the regression in the multidimensional case.
Under suitable condi...