Generalized fiducial inference on the mean of zero-inflated Poisson and Poisson hurdle models

Journal of Statistical Distributions and Applications

Jan Hannig¹  Derek S. Young²  Yixuan Zou³ 

[1] Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA;Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington, Kentucky, USA;Genentech, South San Francisco, California, USA;

关键词: Count data;    Coverage probability;    Data dispersion;    Generalized confidence intervals;    Zero-truncated poisson;   

DOI  :  10.1186/s40488-021-00117-0

来源: Springer

【 摘 要 】

Zero-inflated and hurdle models are widely applied to count data possessing excess zeros, where they can simultaneously model the process from how the zeros were generated and potentially help mitigate the effects of overdispersion relative to the assumed count distribution. Which model to use depends on how the zeros are generated: zero-inflated models add an additional probability mass on zero, while hurdle models are two-part models comprised of a degenerate distribution for the zeros and a zero-truncated distribution. Developing confidence intervals for such models is challenging since no closed-form function is available to calculate the mean. In this study, generalized fiducial inference is used to construct confidence intervals for the means of zero-inflated Poisson and Poisson hurdle models. The proposed methods are assessed by an intensive simulation study. An illustrative example demonstrates the inference methods.

【 授权许可】

CC BY