Famoye, Felix; Singh, Karan P. On inflated generalized Poisson regression models. (English) Zbl 1042.62063 Adv. Appl. Stat. 3, No. 2, 145-158 (2003). Summary: Zero-inflated Poisson and zero-inflated negative binomial regression models have been proposed for data sets that result into too many zeros. Recently, the zero-inflated generalized Poisson regression model is defined as a good alternate to model count data with too many zeros [the authors, Zero inflated generalized Poisson regression model. submitted]. In this paper, we propose a \(k\)-inflated generalized Poisson regression to model count data with too many \(k\)-values. Estimation of the model parameters using the method of maximum likelihood is provided. A score test is presented to test whether the number of \(k\)-values is too large for the generalized Poisson model to adequately fit the data. The \(k\)-inflated generalized Poisson regression model is illustrated using a dataset with too many ones. Cited in 14 Documents MSC: 62J02 General nonlinear regression 62F10 Point estimation 62J12 Generalized linear models (logistic models) 62P10 Applications of statistics to biology and medical sciences; meta analysis Keywords:count data; \(k\)-inflation; hypothesis testing PDFBibTeX XMLCite \textit{F. Famoye} and \textit{K. P. Singh}, Adv. Appl. Stat. 3, No. 2, 145--158 (2003; Zbl 1042.62063)