Comptes Rendus
Statistiques
Dimension reduction in spatial regression with kernel SAVE method
[Réduction de la dimension en régression spatiale avec la méthode SAVE à noyau]
Mètolidji Moquilas Raymond Affossogbe1 ; Guy Martial Nkiet2  ; Carlos Ogouyandjou1
1 Institut de Mathématiques et de Sciences Physiques,Porto Novo, Bénin
2 Université des Sciences et Techniques de Masuku, Franceville, Gabon
Comptes Rendus. Mathématique, Volume 359 (2021) no. 4, pp. 475-479.

Nous considérons la version lisse de la méthode SAVE pour prendre en compte des observations spatialement dépendantes émanant d’un champ aléatoire fortement mélangeant. Nous proposons des estimateurs à noyau pour la matrice d’intérêt et l’espace de rédution de la dimension, et montrons leur convergence.

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.

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DOI : 10.5802/crmath.187
Mètolidji Moquilas Raymond Affossogbe 1 ; Guy Martial Nkiet 2 ; Carlos Ogouyandjou 1

1 Institut de Mathématiques et de Sciences Physiques,Porto Novo, Bénin
2 Université des Sciences et Techniques de Masuku, Franceville, Gabon
Licence : CC-BY 4.0
Droits d'auteur : Les auteurs conservent leurs droits 
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     title = {Dimension reduction in spatial regression with kernel {SAVE} method},
     journal = {Comptes Rendus. Math\'ematique},
     pages = {475--479},
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Mètolidji Moquilas Raymond Affossogbe; Guy Martial Nkiet; Carlos Ogouyandjou. Dimension reduction in spatial regression with kernel SAVE method. Comptes Rendus. Mathématique, Volume 359 (2021) no. 4, pp. 475-479. doi : 10.5802/crmath.187. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.187/

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