Nonparametric estimation for stationary and strongly mixing processes on Riemannian manifolds

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AuteurGBAGUIDI AMOUSSOU, AMOUR
AuteurDJIBRIL MOUSSA, FREEDATH LAYE ERI YOMI
AuteurOGOUYANDJOU, CARLOS
AuteurDIOP, MAMADOU ABDOUL
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2022
ResumeIn this paper, nonparametric estimation for a stationary strongly mixing and manifoldvalued process (X j ) is considered. In 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 process. We prove the strong consistency of these estimators under sufficient conditions, and we illustrate their performance through simulation studies and real data analysis.
DOI10.1007/s40304-020-00237-0
Autre identifiantBECDB-14342
URIhttps://dspace.uac.bj/handle/123456789/12240
Languefr
Fait partie deCommunications in Mathematics and Statistics
SujetRiemannian manifolds · Nonparametric estimation · Kernel density
Sujetestimation · Stationary and strongly mixing processes · Strong consistency
TitreNonparametric estimation for stationary and strongly mixing processes on Riemannian manifolds
TypeArticle

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