Parametric models for distributional data

Brito, Paula; Silva, A. Pedro Duarte

Publication

Parametric models for distributional data

2025-03-10Research article

dc.contributor.author	Brito, Paula
dc.contributor.author	Silva, A. Pedro Duarte
dc.date.accessioned	2025-03-27T18:08:18Z
dc.date.available	2025-03-27T18:08:18Z
dc.date.issued	2025-03-10
dc.description.abstract	We present parametric probabilistic models for numerical distributional variables. The proposed models are based on the representation of each distribution by a location measure and inter-quantile ranges, for given quantiles, thereby characterizing the underlying empirical distributions in a flexible way. Multivariate Normal distributions are assumed for the whole set of indicators, considering alternative structures of the variance–covariance matrix. For all cases, maximum likelihood estimators of the corresponding parameters are derived. This modelling allows for hypothesis testing and multivariate parametric analysis. The proposed framework is applied to Analysis of Variance and parametric Discriminant Analysis of distributional data. A simulation study examines the performance of the proposed models in classification problems under different data conditions. Applications to Internet traffic data and Portuguese official data illustrate the relevance of the proposed approach.	eng
dc.identifier.doi	10.1007/s11634-025-00624-x
dc.identifier.eid	86000521435
dc.identifier.issn	1862-5347
dc.identifier.uri	http://hdl.handle.net/10400.14/52841
dc.identifier.wos	001440802100001
dc.language.iso	eng
dc.peerreviewed	yes
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Analysis of variance
dc.subject	Discriminant analysis
dc.subject	Histogram data
dc.subject	Symbolic data
dc.title	Parametric models for distributional data	eng
dc.type	research article
dspace.entity.type	Publication
oaire.citation.title	Advances in Data Analysis and Classification
oaire.version	http://purl.org/coar/version/c_970fb48d4fbd8a85