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Goodness-of-fit test for a-stable distribution based on the quantile conditional variance statistics
α-stable distribution
heavy-tailed distribution
conditional variance
trimmed variance
quantile conditional variance
goodness-of-fit test
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.
cris.lastimport.wos | 2024-04-09T21:04:05Z | |
dc.abstract.en | 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. | pl |
dc.affiliation | Wydział Matematyki i Informatyki : Instytut Matematyki | pl |
dc.contributor.author | Pitera, Marcin - 107421 | pl |
dc.contributor.author | Chechkin, Aleksei | pl |
dc.contributor.author | Wyłomaśka, Agnieszka | pl |
dc.date.accessioned | 2022-07-08T12:17:22Z | |
dc.date.available | 2022-07-08T12:17:22Z | |
dc.date.issued | 2022 | pl |
dc.date.openaccess | 0 | |
dc.description.accesstime | w momencie opublikowania | |
dc.description.physical | 387-424 | pl |
dc.description.version | ostateczna wersja wydawcy | |
dc.description.volume | 31 | pl |
dc.identifier.doi | 10.1007/s10260-021-00571-9 | pl |
dc.identifier.eissn | 1613-981X | pl |
dc.identifier.issn | 1618-2510 | pl |
dc.identifier.uri | https://ruj.uj.edu.pl/xmlui/handle/item/295579 | |
dc.language | eng | pl |
dc.language.container | eng | pl |
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.en | α-stable distribution | pl |
dc.subject.en | heavy-tailed distribution | pl |
dc.subject.en | conditional variance | pl |
dc.subject.en | trimmed variance | pl |
dc.subject.en | quantile conditional variance | pl |
dc.subject.en | goodness-of-fit test | pl |
dc.subtype | Article | pl |
dc.title | Goodness-of-fit test for a-stable distribution based on the quantile conditional variance statistics | pl |
dc.title.journal | Statistical Methods & Applications | pl |
dc.type | JournalArticle | pl |
dspace.entity.type | Publication |
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