Paired count regressions for modeling the number of doctor consultations and non-prescribed drugs intake.

ABSTRACT: There are sundry practical situations in which paired count variables are correlated, thus requiring a joint estimation method. In this article, we introduce a flexible class of bivariate mixed Poisson regression models, which settle into an exponential-family (EF) distributed component for unobserved heterogeneity. The proposed bivariate mixed Poisson models deal with the phenomenon of overdispersion, typical of count data, and have flexibility in terms of the correlation structure. Thus, this novel class of models has a distinct advantage over the most widely used models because it captures both positive and negative correlations in the count data. Under the bivariate mixed Poisson model, inference of the parameters is conducted through the maximum likelihood method. Monte Carlo studies

SUBMITTER: Goncalves JN 

PROVIDER: S-EPMC12365358 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature