Alternative skew Laplace scale mixtures for modeling data exhibiting high-peaked and heavy-tailed traits

dc.contributor.authorOtto, A.F.
dc.contributor.authorBekker, Andriette, 1958-
dc.contributor.authorFerreira, Johannes Theodorus
dc.contributor.authorArslan, O.
dc.contributor.emailjohan.ferreira@up.ac.zaen_US
dc.date.accessioned2025-03-20T06:01:52Z
dc.date.available2025-03-20T06:01:52Z
dc.date.issued2024-11
dc.descriptionDATA AVAILABILITY : All datasets considered in this paper are freely available on the internet.en_US
dc.description.abstractThe search and construction of appropriate and flexible models for describing and modelling empirical data sets incongruent with normality retains a sustained interest. This paper focuses on proposing flexible skew Laplace scale mixture distributions to model these types of data sets. Each member of the collection of distributions is obtained by dividing the scale parameter of a conditional skew Laplace distribution by a purposefully chosen mixing random variable. Highly-peaked, heavy-tailed skew models with relevance and impact in different fields are obtained and investigated, and elegant sampling schemes to simulate from this collection of developed models are proposed. Finite mixtures consisting of the members of the skew Laplace scale mixture models are illustrated, further extending the flexibility of the distributions by being able to account for multimodality. The maximum likelihood estimates of the parameters for all the members of the developed models are described via a developed EM algorithm. Real-data examples highlight select models’ performance and emphasize their viability compared to other commonly considered candidates, and various goodness-of-fit measures are used to endorse the performance of the proposed models as reasonable and viable candidates for the practitioner. Finally, an outline is discussed for future work in the multivariate realm for these models.en_US
dc.description.departmentStatisticsen_US
dc.description.librarianam2024en_US
dc.description.sdgSDG-13:Climate actionen_US
dc.description.sdgSDG-17:Partnerships for the goalsen_US
dc.description.sponsorshipIn part by the National Research Foundation (NRF) of South Africa (SA); the Department of Research and Innovation at the University of Pretoria (SA), as well as the Centre of Excellence in Mathematical and Statistical Sciences based at the University of the Witwatersrand (SA). Open access funding provided by University of Pretoria.en_US
dc.description.urihttps://www.springer.com/statistics/journal/42081en_US
dc.identifier.citationOtto, A.F., Bekker, A., Ferreira, J.T. et al. 2024, 'Alternative skew Laplace scale mixtures for modeling data exhibiting high-peaked and heavy-tailed traits', Japanese Journal of Statistics and Data Science, vol. 7, pp. 701-738. https://DOI.org/10.1007/s42081-024-00251-4.en_US
dc.identifier.issn2520-8756 (print)
dc.identifier.issn2520-8764 (online)
dc.identifier.other10.1007/s42081-024-00251-4
dc.identifier.urihttp://hdl.handle.net/2263/101617
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© The Author(s) 2024. Open access. This article is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.subjectBodily injury claims dataen_US
dc.subjectContaminated modelen_US
dc.subjectDonor ideology dataen_US
dc.subjectFinite mixturesen_US
dc.subjectHeavy-tailed distributionsen_US
dc.subjectScale mixturesen_US
dc.subjectSDG-13: Climate actionen_US
dc.titleAlternative skew Laplace scale mixtures for modeling data exhibiting high-peaked and heavy-tailed traitsen_US
dc.typeArticleen_US

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