ABSTRACT This paper proposes a data transformation to deal with the presence of constant under/over-dispersion relative to the Poisson or binomial assumptions. The proposed methodology is very simple as it does not require to replace the Poisson or binomial by more complex regression models based on more flexible distributions. The new approach consist of a transformation of the response variable, followed by the analysis of its relation with the covariates by applying to the transformed variable the usual Poisson or binomial regression models. The transformation depends on a tuning parameter, which can be easily chosen using a straightforward criterion. The efectiveness of the proposed approach is illustrated by simulation experiments and by analyzing six real data sets.

中文翻译：

---

处理泊松和二项式回归模型中恒定欠/过分散的数据转换

摘要 本文提出了一种数据转换，以处理相对于泊松或二项式假设的恒定欠/过分散的存在。所提出的方法非常简单，因为它不需要用基于更灵活分布的更复杂的回归模型来替换泊松或二项式。新方法包括响应变量的转换，然后通过将通常的泊松或二项式回归模型应用于转换后的变量来分析其与协变量的关系。转换取决于调整参数，可以使用简单的标准轻松选择该参数。通过模拟实验和分析六个真实数据集来说明所提出方法的有效性。