A flexible regression model for zero- and k-inflated count data. Issue 9 (13th June 2021)

• Record Type: Journal Article
• Authors: Arora, Monika; Rao Chaganty, N.; Sellers, Kimberly F.
• Is Part Of: Journal of statistical computation and simulation. Volume 91:Issue 9(2021)
• Page Start: 1815
• Page End: 1845
• Publication Date: 2021-06-13
• Subjects: Zero-inflated regression models -- Conway–Maxwell–Poisson distribution -- over-dispersion -- under-dispersion; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2021.1872077
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : Count data with inflated zeros commonly occur in numerous research studies. Accordingly, there is substantive literature regarding zero-inflated Poisson and analogous generalizable count regression models that account for data dispersion via excess zeros. Scenarios exist, however, where another count k >0 tends to be inflated, thus there remains the need to develop a flexible regression model that can accommodate both inflated frequencies and any inherent data dispersion. This work achieves this goal by employing the Conway–Maxwell–Poisson (CMP) distribution. We develop a zero- and k -inflated Conway–Maxwell–Poisson (ZkICMP) distribution and corresponding regression that addresses over- and under-dispersed count data. We further discuss parameter estimation and other diagnostics by analytical and numerical methods, and illustrate superior performance of the ZkICMP regression via real data examples.