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Gram Charlier and Edgeworth expansion for sample variance

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Date
2018
Notes
Texte intégral sur le site: https://papers.ssrn.com
Dewey
Principes généraux des mathématiques
Sujet
sample variance; Edgeworth expansion
Journal issue
Theoretical Mathematics and Applications
Volume
8
Number
4
Publication date
12-2018
Article pages
17-31
Publisher
Scienpress
URI
https://basepub.dauphine.fr/handle/123456789/19396
Collections
  • LAMSADE : Publications
Metadata
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Author
Benhamou, Eric
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Article accepté pour publication ou publié
Abstract (EN)
In this paper, we derive a valid Edgeworth expansions for the Bessel corrected empirical variance when data are generated by a strongly mixing process whose distribution can be arbitrarily. The constraint of strongly mixing process makes the problem not easy. Indeed, even for a strongly mixing normal process, the distribution is unknown. Here, we do not assume any other assumption than a sufficiently fast decrease of the underlying distribution to make the Edgeworth expansion convergent. This results can obviously apply to strongly mixing normal process and provide an alternative to the work of Moschopoulos (1985) and Mathai (1982).

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