Aggregate Kernel Inverse Regression Estimation

Wenjuan Li, Wenying Wang, Jingsi Chen and Weidong Rao ()
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Wenjuan Li: School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China
Wenying Wang: School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China
Jingsi Chen: School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China
Weidong Rao: School of Mathematics and Computer Science, Jiangxi Science and Technology Normal University, Nanchang 330038, China

Mathematics, 2023, vol. 11, issue 12, 1-10

Abstract: Sufficient dimension reduction (SDR) is a useful tool for nonparametric regression with high-dimensional predictors. Many existing SDR methods rely on some assumptions about the distribution of predictors. Wang et al. proposed an aggregate dimension reduction method to reduce the dependence on the distributional assumptions. Motivated by their work, we propose a novel and effective method by combining the aggregate method and the kernel inverse regression estimation. The proposed approach can accurately estimate the dimension reduction directions and substantially improve the exhaustivity of the estimates with complex models. At the same time, this method does not depend on the arrangement of slices, and the influence of the extreme values of the response is reduced. In numerical examples and a real data application, it performs well.

Keywords: aggregate kernel inverse regression estimation; kernel inverse regression; aggregate dimension reduction; sufficient dimension reduction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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