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Dimension reduction in spatial regression with kernel SAVE method
- Source :
- Comptes Rendus. Mathématique, Vol 359, Iss 4, Pp 475-479 (2021)
- Publication Year :
- 2021
- Publisher :
- Académie des sciences, 2021.
-
Abstract
- We consider the smoothed version of sliced average variance estimation (SAVE) dimension reduction method for dealing with spatially dependent data that are observations of a strongly mixing random field. We propose kernel estimators for the interest matrix and the effective dimension reduction (EDR) space, and show their consistency.
- Subjects :
- Mathematics
QA1-939
Subjects
Details
- Language :
- English, French
- ISSN :
- 17783569
- Volume :
- 359
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- Comptes Rendus. Mathématique
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.1d284fe2fdc4c3aa84c75bd8ea07d7f
- Document Type :
- article
- Full Text :
- https://doi.org/10.5802/crmath.187