AN ADAPTIVE COMPOSITE QUANTILE APPROACH TO DIMENSION REDUCTION

Kong, Efang (2014) AN ADAPTIVE COMPOSITE QUANTILE APPROACH TO DIMENSION REDUCTION. Annals of Statistics, 42 (4). pp. 1657-1688. ISSN 0090-5364. (Full text available)

Abstract

Su�cient dimension reduction [Li (1991)] has long been a promi- nent issue in multivariate nonparametric regression analysis. To un- cover the central dimension reduction space, we propose in this pa- per an adaptive composite quantile approach. Compared to existing methods, (1) it requires minimal assumptions and is capable of reveal- ing all dimension reduction directions; (2) it is robust against outliers; and (3) it is structure-adaptive, thus more e�cient. Asymptotic re- sults are proved and numerical examples are provided, including a real data analysis.

Item Type:	Article
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions:	Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User:	Efang Kong
Date Deposited:	29 May 2014 13:44 UTC
Last Modified:	09 Jun 2015 12:55 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/41213 (The current URI for this page, for reference purposes)