On the extension of sliced average variance estimation to multivariate regression

Jae Keun Yoo; Keunbaik Lee; Seongho Wu

doi:10.1007/s10260-010-0145-9

On the extension of sliced average variance estimation to multivariate regression

Jae Keun Yoo, Keunbaik Lee, Seongho Wu

Department of Statistics

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

Many sufficient dimension reduction methods for univariate regression have been extended to multivariate regression. Sliced average variance estimation (SAVE) has the potential to recover more reductive information and recent development enables us to test the dimension and predictor effects with distributions commonly used in the literature. In this paper, we aim to extend the functionality of the SAVE to multivariate regression. Toward the goal, we propose three new methods. Numerical studies and real data analysis demonstrate that the proposed methods perform well.

Original language	English
Pages (from-to)	529-540
Number of pages	12
Journal	Statistical Methods and Applications
Volume	19
Issue number	4
DOIs	https://doi.org/10.1007/s10260-010-0145-9
State	Published - 2010

Bibliographical note

Funding Information:
Acknowledgements For the corresponding author Jae Keun Yoo, this work was partially supported by the Ewha Womans University Research Grant of 2010 and was supported by Basic Science Research Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and Technology (2010-0003189). The authors are grateful to the referees for many helpful comments.

Keywords

Double slicing
K-means clustering
Multivariatere gression
Pooled estimation
Sliced average variance estimation

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JO - Statistical Methods and Applications

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On the extension of sliced average variance estimation to multivariate regression

Abstract

Bibliographical note

Keywords

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