Fused sliced average variance estimation

Hyoin An; Sungmin Won; Jae Keun Yoo

doi:10.1016/j.jkss.2017.06.003

Fused sliced average variance estimation

Hyoin An, Sungmin Won, Jae Keun Yoo

Department of Statistics

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

3 Scopus citations

Abstract

In this paper, we propose an approach to combine the kernel matrices constructed by sliced average variance estimation (SAVE) with various numbers of slices. The proposed approach is called fused sliced average variance estimation (FSAVE). By fusing the information by usual SAVE applications with different slice numbers, the sensitivity to slices can be reduced, so the structural dimension estimation can be improved. Numerical studies confirm this, and a real data analysis is presented.

Original language	English
Pages (from-to)	623-628
Number of pages	6
Journal	Journal of the Korean Statistical Society
Volume	46
Issue number	4
DOIs	https://doi.org/10.1016/j.jkss.2017.06.003
State	Published - Dec 2017

Bibliographical note

Publisher Copyright:
© 2017 The Korean Statistical Society

Keywords

Fusing
Inverse regression
Sliced average variance estimation
Sufficient dimension reduction

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10.1016/j.jkss.2017.06.003

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title = "Fused sliced average variance estimation",

abstract = "In this paper, we propose an approach to combine the kernel matrices constructed by sliced average variance estimation (SAVE) with various numbers of slices. The proposed approach is called fused sliced average variance estimation (FSAVE). By fusing the information by usual SAVE applications with different slice numbers, the sensitivity to slices can be reduced, so the structural dimension estimation can be improved. Numerical studies confirm this, and a real data analysis is presented.",

keywords = "Fusing, Inverse regression, Sliced average variance estimation, Sufficient dimension reduction",

author = "Hyoin An and Sungmin Won and Yoo, {Jae Keun}",

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AU - An, Hyoin

AU - Won, Sungmin

AU - Yoo, Jae Keun

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AB - In this paper, we propose an approach to combine the kernel matrices constructed by sliced average variance estimation (SAVE) with various numbers of slices. The proposed approach is called fused sliced average variance estimation (FSAVE). By fusing the information by usual SAVE applications with different slice numbers, the sensitivity to slices can be reduced, so the structural dimension estimation can be improved. Numerical studies confirm this, and a real data analysis is presented.

KW - Fusing

KW - Inverse regression

KW - Sliced average variance estimation

KW - Sufficient dimension reduction

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JO - Journal of the Korean Statistical Society

JF - Journal of the Korean Statistical Society

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ER -

Fused sliced average variance estimation

Abstract

Bibliographical note

Keywords

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