Nonparametric -Divergence Estimation and Test for Model Selection

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dc.contributor.authorDJIBRIL MOUSSA, FREEDATH LAYE
dc.contributor.authorNkurunziza, Jean de Dieu
dc.contributor.authorNgom, Papa
dc.date.accessioned2026-06-02T16:06:57Z
dc.date.available2026-06-02T16:06:57Z
dc.date.issued2020
dc.description.abstractIn this paper, we study a bias reduced kernel density estimator and derive a nonparametric -divergence estimator based on this density estimator. We investigate the asymptotic properties of these two estimators and we formulate an asymptotically standard normal test for model selection.
dc.identifier.otherBECDB-14923
dc.identifier.urihttps://dspace.uac.bj/handle/123456789/12690
dc.language.isofr
dc.relation.ispartofAfrika Statistika
dc.subjectnonparametric estimation
dc.subject-divergence
dc.subjectstrong consistency
dc.subjectasymptotic
dc.subjectnormality
dc.subjecthypothesis testing
dc.subjectmodel Selection
dc.titleNonparametric -Divergence Estimation and Test for Model Selection
dc.typeArticle

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