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.
Lue, H. H. (2009). Sliced inverse regression for multivariate response regression. Journal of Statistical Planning and Inference, 139(8), 2656–2664. https://doi.org/10.1016/j.jspi.2008.12.006