Convex optimization methods for dimension reduction and coefficient estimation in multivariate linear regression
نویسندگان
چکیده
منابع مشابه
Convex optimization methods for dimension reduction and coefficient estimation in multivariate linear regression
In this paper, we study convex optimization methods for computing the nuclear (or, trace) norm regularized least squares estimate in multivariate linear regression. The so-called factor estimation and selection (FES) method, recently proposed by Yuan et al. [25], conducts parameter estimation and factor selection simultaneously and have been shown to enjoy nice properties in both large and fini...
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We introduce a general formulation for dimension reduction and coefficient estimation in the multivariate linear model. We argue that many of the existing methods that are commonly used in practice can be formulated in this framework and have various restrictions. We continue to propose a new method that is more flexible and more generally applicable. The method proposed can be formulated as a ...
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ژورنال
عنوان ژورنال: Mathematical Programming
سال: 2010
ISSN: 0025-5610,1436-4646
DOI: 10.1007/s10107-010-0350-1