Nonparametric estimation for stationary and strongly mixing processes on Riemannian manifolds

作     者：Amour T.Gbaguidi Amoussou Freedath Djibril Moussa Carlos Ogouyandjou Mamadou Abdoul Diop 

作者机构：IMSPUniversitéd’Abomey-Calavi(UAC)DangboBenin FASTUniversitéd’Abomey-CalaviAbomey-CalaviBenin UniversitéGaston BergerDakarSenegal 

出 版 物：《Communications in Mathematics and Statistics》 (数学与统计通讯（英文）)

年 卷 期：2022年第10卷第4期

页      面：599-621页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Riemannian manifolds Nonparametric estimation Kernel density estimation Stationary and strongly mixing processes Strong consistency 

摘      要：In this paper,nonparametric estimation for a stationary strongly mixing and manifoldvalued process(X_(j))is *** this non-Euclidean and not necessarily i.i.d setting,we propose kernel density estimators of the joint probability density function,of the conditional probability density functions and of the conditional expectations of functionals of X_(j)given the past behavior of the *** prove the strong consistency of these estimators under sufficient conditions,and we illustrate their performance through simulation studies and real data analysis.