Abstract This paper provides a review of the literature regarding methods for constructing prediction intervals counting variables, with particular focus on those whose distributions are Poisson or derived from and an over‐dispersion property. Independent identically distributed models regression both considered. The motivating problem this is that predicting number daily cumulative cases death...

Abstract With the emergence of telematics car driving data, insurance companies have started to boost classical actuarial regression models for claim frequency prediction with information. In this paper, we propose two data-driven neural network approaches that process data complement pricing a behavior risk factor from data. Our networks simultaneously accommodate feature engineering and model...

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which render the standard ZIP model...

This study integrates association rules analysis, a methodology for selecting potential interactions, with Poisson regression modeling. Though typically ignored in conventional Poisson regression, interactions are very common in practice. However, selecting a Poisson regression model when many main effects and interactions are involved is problematic. In this study, we develop a model selection...