Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149617 | Journal of Statistical Planning and Inference | 2009 | 9 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Heng-Hui Lue,