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Showing 1 to 4 of 4 for “"Reduced Rank Regression"”.
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Commonality in Two-Dimensions: An Empirical Investigation
… follow Hasbrouck and Seppi (2001)’s work and use reduced-rank regression to model the commonality in Chapter Two. The literature on the study of return commonality generally attributes its source to the order flow. But I find that return and order flows are endogenous and use the new exogenous …
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Bayesian Model Averaging and Variable Selection in Multivariate Ecological Models
… of the multivari-ate technique called Reduced-Rank Regression (RRR). In particular, we are concerned with Canonical Correspondence Analysis (CCA) in ecological applications where the data are represented by a site by species abundance matrix with site-specific covariates. Our goal is to …
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Estimation of Shared Functional Information Between Neural Areas
… explore the distributed coding idea, we employ Reduced Rank Regression (RRR) to define a novel connectivity metric based on how well the activity of a target region can be predicted from a low-dimensional subspace of a source region. This framework allows us to characterize distributed signals …
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Integer and Matrix Optimization: A Nonlinear Approach
… variables z of the form "x=0 if z=0'', or (b) rank constraints. Indeed, start-up costs in machine scheduling and financial transaction costs exhibit logical relations, while important problems such as reduced rank regression and matrix completion contain rank constraints. These constraints are …