Modeling health survey data with excessive zero and K responses.
Zero-inflated Poisson regression is a popular tool used to analyze data with excessive zeros. Although much work has already been performed to fit zero-inflated data, most models heavily depend on special features of the individual data. To be specific, this means that there is a sizable group of respondents who endorse the same answers making the data have peaks. In this paper, we propose a new model with the flexibility to model excessive counts other than zero, and the model is a mixture of multinomial logistic and Poisson regression, in which the multinomial logistic component models the occurrence of excessive counts, including zeros, K (where K is a positive integer) and all other values. The Poisson regression component models the counts that are assumed to follow a Poisson distribution. Two examples are provided to illustrate our models when the data have counts containing many ones and sixes. As a result, the zero-inflated and K-inflated models exhibit a better fit than the zero-inflated Poisson and standard Poisson regressions.
Department of Statistics, National Taipei University, 151 University Rd., San Shia District New Taipei City, 23741,Taiwan. tinghlin@mail.ntpu.edu.tw
Ting Hsiang
TH
Lin
Min-Hsiao
MH
Tsai
D006306:Health Surveys / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis