Random effect exponentiated-exponential geometric model for clustered/longitudinal zero-inflated count data. Issue 12 (9th September 2020)

• Record Type: Journal Article
• Authors: Tapak, Leili; Hamidi, Omid; Amini, Payam; Verbeke, Geert
• Is Part Of: Journal of applied statistics. Volume 47:Issue 12(2020)
• Page Start: 2272
• Page End: 2288
• Publication Date: 2020-09-09
• Subjects: Count model -- under- and over-dispersion -- zero-inflation -- mixture model -- zero-inflated poisson model; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2019.1706726
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : For count responses, there are situations in biomedical and sociological applications in which extra zeroes occur. Modeling correlated (e.g. repeated measures and clustered) zero-inflated count data includes special challenges because the correlation between measurements for a subject or a cluster needs to be taken into account. Moreover, zero-inflated count data are often faced with over/under dispersion problem. In this paper, we propose a random effect model for repeated measurements or clustered data with over/under dispersed response called random effect zero-inflated exponentiated-exponential geometric regression model. The proposed method was illustrated through real examples. The performance of the model and asymptotical properties of the estimations were investigated using simulation studies.