Goodness-of-fit test for a-stable distribution based on the quantile conditional variance statistics

2022
journal article
article
17
cris.lastimport.wos2024-04-09T21:04:05Z
dc.abstract.enThe class of α-stable distributions is ubiquitous in many areas including signal processing, finance, biology, physics, and condition monitoring. In particular, it allows efficient noise modeling and incorporates distributional properties such as asymmetry and heavy-tails. Despite the popularity of this modeling choice, most statistical goodness-of-fit tests designed for α-stable distributions are based on a generic distance measurement methods. To be efficient, those methods require large sample sizes and often do not efficiently discriminate distributions when the corresponding α-stable parameters are close to each other. In this paper, we propose a novel goodness-of-fit method based on quantile (trimmed) conditional variances that is designed to overcome these deficiencies and outperforms many benchmark testing procedures. The effectiveness of the proposed approach is illustrated using extensive simulation study with focus set on the symmetric case. For completeness, an empirical example linked to plasma physics is provided.pl
dc.affiliationWydział Matematyki i Informatyki : Instytut Matematykipl
dc.contributor.authorPitera, Marcin - 107421 pl
dc.contributor.authorChechkin, Alekseipl
dc.contributor.authorWyłomaśka, Agnieszkapl
dc.date.accessioned2022-07-08T12:17:22Z
dc.date.available2022-07-08T12:17:22Z
dc.date.issued2022pl
dc.date.openaccess0
dc.description.accesstimew momencie opublikowania
dc.description.physical387-424pl
dc.description.versionostateczna wersja wydawcy
dc.description.volume31pl
dc.identifier.doi10.1007/s10260-021-00571-9pl
dc.identifier.eissn1613-981Xpl
dc.identifier.issn1618-2510pl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/295579
dc.languageengpl
dc.language.containerengpl
dc.rightsUdzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa*
dc.rights.licenceCC-BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcode.pl*
dc.share.typeinne
dc.subject.enα-stable distributionpl
dc.subject.enheavy-tailed distributionpl
dc.subject.enconditional variancepl
dc.subject.entrimmed variancepl
dc.subject.enquantile conditional variancepl
dc.subject.engoodness-of-fit testpl
dc.subtypeArticlepl
dc.titleGoodness-of-fit test for a-stable distribution based on the quantile conditional variance statisticspl
dc.title.journalStatistical Methods & Applicationspl
dc.typeJournalArticlepl
dspace.entity.typePublication
cris.lastimport.wos
2024-04-09T21:04:05Z
dc.abstract.enpl
The class of α-stable distributions is ubiquitous in many areas including signal processing, finance, biology, physics, and condition monitoring. In particular, it allows efficient noise modeling and incorporates distributional properties such as asymmetry and heavy-tails. Despite the popularity of this modeling choice, most statistical goodness-of-fit tests designed for α-stable distributions are based on a generic distance measurement methods. To be efficient, those methods require large sample sizes and often do not efficiently discriminate distributions when the corresponding α-stable parameters are close to each other. In this paper, we propose a novel goodness-of-fit method based on quantile (trimmed) conditional variances that is designed to overcome these deficiencies and outperforms many benchmark testing procedures. The effectiveness of the proposed approach is illustrated using extensive simulation study with focus set on the symmetric case. For completeness, an empirical example linked to plasma physics is provided.
dc.affiliationpl
Wydział Matematyki i Informatyki : Instytut Matematyki
dc.contributor.authorpl
Pitera, Marcin - 107421
dc.contributor.authorpl
Chechkin, Aleksei
dc.contributor.authorpl
Wyłomaśka, Agnieszka
dc.date.accessioned
2022-07-08T12:17:22Z
dc.date.available
2022-07-08T12:17:22Z
dc.date.issuedpl
2022
dc.date.openaccess
0
dc.description.accesstime
w momencie opublikowania
dc.description.physicalpl
387-424
dc.description.version
ostateczna wersja wydawcy
dc.description.volumepl
31
dc.identifier.doipl
10.1007/s10260-021-00571-9
dc.identifier.eissnpl
1613-981X
dc.identifier.issnpl
1618-2510
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/295579
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa
dc.rights.licence
CC-BY
dc.rights.uri*
http://creativecommons.org/licenses/by/4.0/legalcode.pl
dc.share.type
inne
dc.subject.enpl
α-stable distribution
dc.subject.enpl
heavy-tailed distribution
dc.subject.enpl
conditional variance
dc.subject.enpl
trimmed variance
dc.subject.enpl
quantile conditional variance
dc.subject.enpl
goodness-of-fit test
dc.subtypepl
Article
dc.titlepl
Goodness-of-fit test for a-stable distribution based on the quantile conditional variance statistics
dc.title.journalpl
Statistical Methods & Applications
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations

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