Parametric models for distributional data

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

brito, p (Autor) FEP; Silva, APD (Autor) Outra

Revista

Título: Advances in Data Analysis and Classification

ISSN: 1862-5347

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-018-85G

DOI: 10.1007/s11634-025-00624-x

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 28