To explore nonlinear structures hidden in high-dimensional data and to estimate the effective dimension reduction directions in multivariate nonparametric regression, Li and Duan proposed the sliced inverse regression (SIR) method which is simple to use. In this paper, the asymptotic properties of the kernel estimate of sliced inverse regression are investigated. It turns out that regardless of the kernel function, the asymptotic distribution remains the same for a wide range of smoothing parameters.
Publié le : 1996-06-14
Classification:
Data structure,
dimension reduction,
kernel estimation,
nonparametric regression,
sliced inverse regression,
60F05,
62G05,
62J02
@article{1032526955,
author = {Zhu, Li-Xing and Fang, Kai-Tai},
title = {Asymptotics for kernel estimate of sliced inverse regression},
journal = {Ann. Statist.},
volume = {24},
number = {6},
year = {1996},
pages = { 1053-1068},
language = {en},
url = {http://dml.mathdoc.fr/item/1032526955}
}
Zhu, Li-Xing; Fang, Kai-Tai. Asymptotics for kernel estimate of sliced inverse regression. Ann. Statist., Tome 24 (1996) no. 6, pp. 1053-1068. http://gdmltest.u-ga.fr/item/1032526955/