Back to Search
Start Over
Recursive kernel estimator in a semiparametric regression model.
- Source :
-
Journal of Nonparametric Statistics . Mar2023, Vol. 35 Issue 1, p145-171. 27p. - Publication Year :
- 2023
-
Abstract
- Sliced inverse regression (SIR) is a recommended method to identify and estimate the central dimension reduction (CDR) subspace. CDR subspace is at the base to describe the conditional distribution of the response Y given a d-dimensional predictor vector X. To estimate this space, two versions are very popular: the slice version and the kernel version. A recursive method of the slice version has already been the subject of a systematic study. In this paper, we propose to study the kernel version. It's a recursive method based on a stochastic approximation algorithm of the kernel version. The asymptotic normality of the proposed estimator is also proved. A simulation study that not only shows the good numerical performance of the proposed estimate and which also allows to evaluate its performance with respect to existing methods is presented. A real dataset is also used to illustrate the approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10485252
- Volume :
- 35
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Nonparametric Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 162056487
- Full Text :
- https://doi.org/10.1080/10485252.2022.2130308