A doubly-inflated Poisson regression for correlated count data

A doubly-inflated Poisson regression for correlated count data

© 2020 Informa UK Limited, trading as Taylor & Francis Group.

Bibliographische Detailangaben
Veröffentlicht in:Journal of applied statistics. - 1991. - 48(2021), 6 vom: 17., Seite 1111-1127
1. Verfasser: Ghasemi, Erfan (VerfasserIn)
Weitere Verfasser: Akbarzadeh Baghban, Alireza, Zayeri, Farid, Pourhoseingholi, Asma, Safavi, Seyed Mohammadreza
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2021
Zugriff auf das übergeordnete Werk:Journal of applied statistics
Schlagworte:Journal Article Count data Poisson regression correlated data doubly-inflated zero-inflated
Beschreibung
Zusammenfassung:© 2020 Informa UK Limited, trading as Taylor & Francis Group.
Count data have emerged in many applied research areas. In recent years, there has been a considerable interest in models for count data. In modelling such data, it is common to face a large frequency of zeroes. The data are regarded as zero-inflated when the frequency of observed zeroes is larger than what is expected from a theoretical distribution such as Poisson distribution, as a standard model for analysing count data. Data analysis, using the simple Poisson model, may lead to over-dispersion. Several classes of different mixture models were proposed for handling zero-inflated data. But they do not apply to cases when inflated counts happen at some other points, in addition to zero. In these cases, a doubly-inflated Poisson model has been suggested which only be used for cross-sectional data and cannot consider correlations between observations. However, correlated count data have a large application, especially in the health and medical fields. The present study aims to introduce a Doubly-Inflated Poisson models with random effect for correlated doubly-inflated data. Then, the best performance of the proposed method is shown via different simulation scenarios. Finally, the proposed model is applied to a dental study
Beschreibung:Date Revised 16.07.2022
published: Electronic-eCollection
Citation Status PubMed-not-MEDLINE
ISSN:0266-4763
DOI:10.1080/02664763.2020.1757049