Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308614 
Year of Publication: 
2022
Citation: 
[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 65 [Issue:] 1 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 45-78
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We introduce a new characterization of the Cauchy distribution and propose a class of goodness-of-fit tests for the Cauchy family. The limit distribution is derived in a Hilbert space framework under the null hypothesis. The new tests are consistent against a large class of alternatives. A comparative Monte Carlo simulation study shows that the test is a good competitor for the state of the art procedures, and we apply the tests to log-returns of cryptocurrencies.
Subjects: 
Goodness-of-fit
Cauchy distribution
Hilbert-space valued random elements
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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