K-depth tests for testing simultaneously independence and other model assumptions in time series

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dc.contributor.authorDohme, Hendrik
dc.contributor.authorMalchercyzk, Dennis
dc.contributor.authorLeckey, Kevin
dc.contributor.authorMüller, Christine H.
dc.date.accessioned2025-08-28T11:12:37Z
dc.date.available2025-08-28T11:12:37Z
dc.date.issued2024-10-23
dc.description.abstractWe consider the recently developed K-depth tests for testing simultaneously independence and other model assumptions for univariate time series with a potentially related d-dimensional process of explanatory variables. Since these tests are based only on signs of residuals, they are easy to comprehend. They can be used in a full version and in a simplified version. While former investigations already showed that the full version is appropriate for testing model assumptions, we concentrate here on either testing the independence assumption on its own or on simultaneously testing independence and model assumptions with both types of tests. In an extensive simulation study, we compare these tests with several known independence test such as the runs test, the Durbin-Watson test, and the Von-Neumann-Rank-Ratio test. Finally, we demonstrate how the K-depth tests can be used for improved modeling of crack width time series depending on temperature measurements in a bridge monitoring.en
dc.identifier.urihttp://hdl.handle.net/2003/43881
dc.language.isoen
dc.relation.ispartofseriesCommunications in statistics
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectK-sign depthen
dc.subjectIndependenceen
dc.subjectTestsen
dc.subjectTime seriesen
dc.subjectModel selectionen
dc.subject.ddc310
dc.titleK-depth tests for testing simultaneously independence and other model assumptions in time seriesen
dc.typeText
dc.type.publicationtypeResearchArticle
dcterms.accessRightsopen access
eldorado.doi.registerfalse
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationDohme, H., Malchercyzk, D., Leckey, K., & Müller, C. H. (2024). K-depth tests for testing simultaneously independence and other model assumptions in time series. Communications in Statistics - Simulation and Computation, 1–19. https://doi.org/10.1080/03610918.2024.2413905
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1080/03610918.2024.2413905

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