General directional regression

Zhou Yu, Yuexiao Dong and Mian Huang

Journal of Multivariate Analysis, 2014, vol. 124, issue C, 94-104

Abstract: Directional regression is an effective sufficient dimension reduction method which implicitly synthesizes the first two conditional moments. In this paper, we extend directional regression to a general family of estimators via the notion of general empirical directions. Data-driven method is used to identify the optimal estimator within this family. Based on the proposed general directional regression estimators, we develop a new methodology for nonlinear dimension reduction. Improvement of general directional regression over classical directional regression is demonstrated via simulation studies and an empirical study with the wine recognition data.

Keywords: General empirical directions; Nonlinear dimension reduction; Permutation test; Sliced inverse regression; Sliced average variance estimation (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X13002236
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:124:y:2014:i:c:p:94-104

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2013.10.016

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().