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.

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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: 29 May 2019 12:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41213 (The current URI for this page, for reference purposes)
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