Computational modeling of epidemiological count data using Non-Homogeneous Poisson Processes and functional data

Computational modeling of epidemiological count data using Non-Homogeneous Poisson Processes and functional data

Blog Article

In this work, we introduce a novel methodology for modeling discrete count variables within the GRUNDIG GKFI7030 Integrated 70/30 Fridge Freezer - White framework of stochastic processes.Our approach integrates two statistical areas: Non-Homogeneous Poisson Processes for the estimation and prediction of intensity functions based on explanatory variables and functional data estimation techniques.Through a comprehensive case study focusing on an infectious disease with viral characteristics, we demonstrate the potential of our methodology.

We provide empirical evidence that our methodology offers a robust alternative for modeling count variables.Our findings support the utility of our approach in capturing the complex dynamics inherent in count data in infectious disease Dreamcast epidemiological phenomena.