Sliced inverse regression for multivariate response regression

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We consider a regression analysis of multivariate response on a vector of predictors. In this article, we develop a sliced inverse regression-based method for reducing the dimension of predictors without requiring a prespecified parametric model. Our proposed method preserves as much regression information as possible. We derive the asymptotic weighted chi-squared test for dimension. Simulation results are reported and comparisons are made with three methods-most predictable variates, k-means inverse regression and canonical correlation approach. © 2009 Elsevier B.V. All rights reserved.

Author-supplied keywords

  • Canonical correlation
  • Dimension reduction
  • Most predictable variates
  • Multivariate response
  • Sliced inverse regression

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  • Heng Hui Lue

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