10.6084/m9.figshare.1150266.v2 Mehreteab Aregay Mehreteab Aregay Ziv Shkedy Ziv Shkedy Geert Molenberghs Geert Molenberghs Comparison of Additive and Multiplicative Bayesian Models for Longitudinal Count Data with Overdispersion Parameters: A Simulation Study Taylor & Francis Group 2014 additive multiplicative model count data Multiplicative Bayesian Models Longitudinal Count Data 2014-09-19 14:08:55 Journal contribution https://tandf.figshare.com/articles/journal_contribution/Comparison_of_Additive_and_Multiplicative_Bayesian_Models_for_Longitudinal_Count_Data_with_Overdispersion_Parameters_A_Simulation_Study/1150266 <div><p>In applied statistical data analysis, overdispersion is a common feature. It can be addressed using both multiplicative and additive random effects. A multiplicative model for count data incorporates a gamma random effect as a multiplicative factor into the mean, whereas an additive model assumes a normally distributed random effect, entered into the linear predictor. Using Bayesian principles, these ideas are applied to longitudinal count data, based on the so-called combined model. The performance of the additive and multiplicative approaches is compared using a simulation study.</p></div>