Dimension Reduction for Nonelliptically Distributed Predictors

  title={Dimension Reduction for Nonelliptically Distributed Predictors},
  author={Bing Li and Yuexiao Dong},
Sufficient dimension reduction methods often require stringent conditions on the joint distribution of the predictor, or, when such conditions are not satisfied, rely on marginal transformation or reweighting to fulfill them approximately. For example, a typical dimension reduction method would require the predictor to have elliptical or even multivariate normal distribution. In this paper, we re-formulate the commonly used dimension reduction methods, via the notion of " central solution space… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 20 references

On directional regression for dimension reduction

B Li, S Wang
J. Amer. Statist. Assoc • 2007

An adaptive estimation of optimal regression subspace Successive direction extraction for estimating the central subspace in a multiple-index regression

Y Xia, H Tong, W K Li, L X Zhu
J. Roy. Statist. Soc. Ser. B J. Multivariate Anal • 2002
View 3 Excerpts

Estimating the structural dimension of regressions via parametric inverse regression Sufficient dimension reduction and graphics in regression

E Bura, R D Cook
J. Roy. Statist. Soc. Ser. B Ann. Inst. Statist. Math • 2001
View 2 Excerpts

Asymptotics for kernel estimate of sliced inverse regression

L.-X Zhu, K.-T Fang
Ann. Statist • 1996
View 1 Excerpt

Similar Papers

Loading similar papers…