Logo do repositório
 
Publicação

Exploring Asymptotic Normality in Multinomial Models

2026-08Artigo científico Acesso aberto
Ver registo simples
dc.contributor.authorNunes, Célia
dc.contributor.authorAkoto, Isaac
dc.contributor.authorSantos, Carla
dc.contributor.authorTiago Mexia, João
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.pblWiley
dc.date.accessioned2026-07-29T13:36:01Z
dc.date.available2026-07-29T13:36:01Z
dc.date.issued2026-08
dc.descriptionPublisher Copyright: © 2026 The Author(s). Mathematical Methods in the Applied Sciences published by John Wiley & Sons Ltd.
dc.description.abstractAmong the methods for analyzing categorical outcomes, the multinomial model offers a robust framework for examining the dependence between a multi-category response variable and a set of explanatory variables. Its flexibility, versatility, and broad applicability across diverse fields make it a valuable tool, as it does not impose strict assumptions. In this work, we focus on deriving a standardized asymptotic distribution for multinomial models, significantly advancing the theoretical framework for categorical data analysis. Building on the concept of smooth statistics—characterized by having components with continuous second-order partial derivatives in a neighborhood of a location parameter—we extend a key theorem on asymptotic normality, originally developed for Wishart matrices, to multinomial models with both finite and countable sets of possible outcomes. A main contribution of this study lies in the standardization process, as it allows addressing the challenges arising from non-invertible covariance matrices, enabling application even starting from singular covariance matrices. This approach significantly advances the analysis of multinomial models by producing a simpler structure through standardized asymptotic distributions, thus broadening the applicability of smooth statistics. These theoretical developments are particularly relevant to the mathematical modeling of categorical processes with spatial and temporal dependence, where evolving states encounter complex dependency structures. The robustness to singular covariance matrices directly addresses challenges common in biomathematical models and others, thereby broadening the mathematical methodology for analyzing structured categorical data in such applied scientific contexts.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent14
dc.format.extent1572940
dc.identifier.doi10.1002/mma.70770
dc.identifier.issn0170-4214
dc.identifier.otherPURE: 169993415
dc.identifier.otherPURE UUID: 17f3ed05-d17f-4a2c-9f06-7b5aaa82b47e
dc.identifier.otherScopus: 105037336503
dc.identifier.urihttp://hdl.handle.net/10362/204942
dc.identifier.urlhttps://www.scopus.com/pages/publications/105037336503
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Avaliação UID 2023%2F2024/UID%2F00212%2F2025/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Avaliação UID 2023%2F2024/UID%2F00297%2F2025/PT
dc.subjectasymptotic normality
dc.subjectmultinomial models
dc.subjectsmooth statistic
dc.subjectstandardization
dc.subjectGeneral Mathematics
dc.subjectGeneral Engineering
dc.titleExploring Asymptotic Normality in Multinomial Modelsen
dc.typejournal article
degois.publication.firstPage13971
degois.publication.issue12
degois.publication.lastPage13984
degois.publication.titleMathematical Methods in the Applied Sciences
degois.publication.volume49
dspace.entity.typePublication
rcaap.rightsopenAccess

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Nunes_et_al._2026_.pdf
Tamanho:
1.5 MB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

Home collection (FCT)