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Joint variable and rank selection for parsimonious estimation of high-dimensional matrices

ANNALS OF STATISTICS, no. 5 (2012): 2359-2388

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We propose dimension reduction methods for sparse, high-dimensional multivariate response regression models. Both the number of responses and that of the predictors may exceed the sample size. Sometimes viewed as complementary, predictor selection and rank reduction are the most popular strategies for obtaining lower-dimensional approxima...更多

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