Statistics
Dimension reduction in spatial regression with kernel SAVE method
Comptes Rendus. Mathématique, Volume 359 (2021) no. 4, pp. 475-479.

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.

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.

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

1 Institut de Mathématiques et de Sciences Physiques,Porto Novo, Bénin
2 Université des Sciences et Techniques de Masuku, Franceville, Gabon
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Affossogbe, Mètolidji Moquilas Raymond; Nkiet, Guy Martial; Ogouyandjou, Carlos. 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. http://www.numdam.org/articles/10.5802/crmath.187/

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