Mean regression model for the zero-truncated Poisson distribution and its generalization

Xun-Jian Li, Yuan Sun, Guo-Liang Tian, Jiajuan Liang and Jianhua Shi

Computational Statistics & Data Analysis, 2023, vol. 179, issue C

Abstract: Zero-truncated count data (e.g., days of staying in hospital; survival weeks of female patients with breast cancer) often arise in various fields such as medical studies. To model such data, the zero-truncated Poisson (ZTP) distribution is commonly utilized to investigate the relationship between the response counts and a set of covariates. For existing ZTP regression models, it is very hard to explain the regression coefficients β or it is quite difficult to perform a constrained optimization to calculate the maximum likelihood estimates (MLEs) of β. This paper aims to introduce a new mean regression model for the ZTP distribution with a clear interpretation about the regression coefficients. Because of a challenge that the original Poisson mean parameter λi cannot be expressed explicitly by the ZTP mean parameter μi, an embedded Newton–Raphson algorithm is developed to calculate the MLEs of regression coefficients. The construction of bootstrap confidence intervals is presented and three hypothesis tests (i.e., the likelihood ratio test, the Wald test and the score test) are considered. Furthermore, the ZTP mean regression model is generalized to the mean regression model for the k-truncated Poisson distribution. Simulation studies are conducted and two real data are analyzed to illustrate the proposed model and methods.

Keywords: Embedded Newton–Raphson algorithm; k-truncated Poisson; Mean regression; Minorization–maximization algorithm; Zero-truncated Poisson (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:179:y:2023:i:c:s0167947322002304

DOI: 10.1016/j.csda.2022.107650

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