Addressing overdispersion and zero-inflation for clustered count data via new multilevel heterogenous hurdle models

Basit Öğe Kaydı
dc.authoridAltinisik, Yasin/0000-0001-9375-2276
dc.contributor.authorAltinisik, Yasin
dc.date.accessioned2025-03-23T19:35:18Z
dc.date.available2025-03-23T19:35:18Z
dc.date.issued2023
dc.departmentSinop Üniversitesi
dc.description.abstractUnobserved heterogeneity causing overdispersion and the excessive number of zeros take a prominent place in the methodological development on count modeling. An insight into the mechanisms that induce heterogeneity is required for better understanding of the phenomenon of overdispersion. When the heterogeneity is sourced by the stochastic component of the model, the use of a heterogenous Poisson distribution for this part encounters as an elegant solution. Hierarchical design of the study is also responsible for the heterogeneity as the unobservable effects at various levels also contribute to the overdispersion. Zero-inflation, heterogeneity and multilevel nature in the count data present special challenges in their own respect, however the presence of all in one study adds more challenges to the modeling strategies. This study therefore is designed to merge the attractive features of the separate strand of the solutions in order to face such a comprehensive challenge. This study differs from the previous attempts by the choice of two recently developed heterogeneous distributions, namely Poisson-Lindley (PL) and Poisson-Ailamujia (PA) for the truncated part. Using generalized linear mixed modeling settings, predictive performances of the multilevel PL and PA models and their hurdle counterparts were assessed within a comprehensive simulation study in terms of bias, precision and accuracy measures. Multilevel models were applied to two separate real world examples for the assessment of practical implications of the new models proposed in this study.
dc.identifier.doi10.1080/02664763.2022.2096875
dc.identifier.endpage433
dc.identifier.issn0266-4763
dc.identifier.issn1360-0532
dc.identifier.issue2
dc.identifier.pmid36698542
dc.identifier.scopus2-s2.0-85134834047
dc.identifier.scopusqualityQ1
dc.identifier.startpage408
dc.identifier.urihttps://doi.org/10.1080/02664763.2022.2096875
dc.identifier.urihttps://hdl.handle.net/11486/5839
dc.identifier.volume50
dc.identifier.wosWOS:000830639800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAltinisik, Yasin
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofJournal of Applied Statistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250323
dc.subjectMultilevel modeling
dc.subjectcount data
dc.subjectoverdispersion
dc.subjectzero-inflation
dc.subjectPoisson-Lindley distribution
dc.subjectPoisson-Ailamujia distribution
dc.titleAddressing overdispersion and zero-inflation for clustered count data via new multilevel heterogenous hurdle models
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu