Divergence Measures Estimation and Its Asymptotic Normality Theory Using Wavelets Empirical Processes III

In the two previous papers of this series, the main results on the asymptotic behaviors of empirical divergence measures based on wavelets theory have been established and particularized for important families of divergence measures like Rényi and Tsallis families and for the Kullback-Leibler measur...

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Vydáno v:Journal of statistical theory and applications Ročník 18; číslo 2; s. 113 - 122
Hlavní autoři: Bâ, Amadou Diadié, Lo, Gane Samb, Bâ, Diam
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.06.2019
Springer Nature B.V
Springer
Témata:
Asymptotic methods
Asymptotic properties
Distribution functions
Divergence
Divergence measures estimation
Normality
Research Article
Divergence measures estimation
ISSN:1538-7887, 2214-1766, 1538-7887
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Popis
Shrnutí:In the two previous papers of this series, the main results on the asymptotic behaviors of empirical divergence measures based on wavelets theory have been established and particularized for important families of divergence measures like Rényi and Tsallis families and for the Kullback-Leibler measures. While the proofs of the results in the second paper may be skipped, the proofs of those in paper 1 are to be thoroughly proved since they serve as a foundation to the whole structure of results. We prove them in this last paper of the series. We will also address the applicability of the results to usual distribution functions.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1538-7887
2214-1766
1538-7887
DOI:10.2991/jsta.d.190514.002