A doubly-inflated Poisson regression for correlated count data. Issue 6 (26th April 2021)

• Record Type: Journal Article
• Authors: Ghasemi, Erfan; Akbarzadeh Baghban, Alireza; Zayeri, Farid; Pourhoseingholi, Asma; Safavi, Seyed Mohammadreza
• Is Part Of: Journal of applied statistics. Volume 48:Issue 6(2021)
• Page Start: 1111
• Page End: 1127
• Publication Date: 2021-04-26
• Subjects: Count data -- doubly-inflated -- Poisson regression -- zero-inflated -- correlated data; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2020.1757049
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : 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.