Dimension reduction in spatial regression with kernel SAVE method

被引：0
|
作者：
Affossogbe, Metolidji Moquilas Raymond [1 ]
Nkiet, Guy Martial [2 ]
Ogouyandjou, Carlos [1 ]
机构：
[1] Inst Math & Sci Phys, Porto Novo, Benin
[2] Univ Sci & Tech Masuku, Franceville, Gabon
来源：
COMPTES RENDUS MATHEMATIQUE | 2021年 / 359卷 / 04期
关键词：
SLICED INVERSE REGRESSION; NONPARAMETRIC-ESTIMATION; DENSITY-ESTIMATION;
D O I：
10.5802/crmath.187
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
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.
引用
收藏
页码：475 / 479
页数：5
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