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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 ...
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 $n < p$.
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...
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 ...
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...

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