About: Non-Central Limit Theorem Statistical Analysis for the%22Long-tailed%22Internet Society

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Non-Central Limit Theorem Statistical Analysis for the%22Long-tailed%22Internet Society
Creator	Kurihara, Kazutaka Tutiya, Yohei
source	ArXiv
abstract	This article presents a statistical analysis method and introduces the corresponding software package%22tailstat,%22which is believed to be widely applicable to today's internet society. The proposed method facilitates statistical analyses with small sample sets from given populations, which render the central limit theorem inapplicable. A large-scale case study demonstrates the effectiveness of the method and provides implications for applying similar analyses to other cases.
has issue date	2010-02-25(xsd:dateTime)
has license	arxiv
sha1sum (hex)	62325ed24d2ee68d5fcce2e7078fad69c9bad3c3
resource representing a document's title	Non-Central Limit Theorem Statistical Analysis for the%22Long-tailed%22Internet Society
resource representing a document's body	covid:62325ed24d2ee68d5fcce2e7078fad69c9bad3c3#body_text
is schema:about of	named entity 'Internet Society' covid:arg/62325ed24d2ee68d5fcce2e7078fad69c9bad3c3 named entity 'case study' named entity 'sets' named entity 'case study' »more»

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