Annals of Data Science, Volume (3), No (4), Year (2016-10) , Pages (401-411)

Title : ( A Goodness-of-Fit Test for Rayleigh Distribution Based on Hellinger Distance )

Authors: Seyed Mahdi Amir Jahanshahi , Arezou Habibirad , Vahid Fakoor ,

Citation: BibTeX | EndNote

In this paper, we i ntroduce a new goodness-of-fit test for Rayleigh distribu-tion based on Hellinger distance. In addition, some properties about the proposed test is presented. Then, new proposed test is compared with other goodness-of-fit t ests for Rayleigh distribution i n t he literature in terms of power. Finally, we conclude that the entropy based tests demonstrate a good performance in terms of power and we can choose t he Hellinger test as more powerful than the other competitor t ests.

Keywords

, Entropy · Goodness, of, fit · Hellinger distance · Power · Rayleigh
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@article{paperid:1058728,
author = {Amir Jahanshahi, Seyed Mahdi and Habibirad, Arezou and Fakoor, Vahid},
title = {A Goodness-of-Fit Test for Rayleigh Distribution Based on Hellinger Distance},
journal = {Annals of Data Science},
year = {2016},
volume = {3},
number = {4},
month = {October},
issn = {2198-5804},
pages = {401--411},
numpages = {10},
keywords = {Entropy · Goodness-of-fit · Hellinger distance · Power · Rayleigh distribution},
}

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%0 Journal Article
%T A Goodness-of-Fit Test for Rayleigh Distribution Based on Hellinger Distance
%A Amir Jahanshahi, Seyed Mahdi
%A Habibirad, Arezou
%A Fakoor, Vahid
%J Annals of Data Science
%@ 2198-5804
%D 2016

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