Modelado computacional de datos epidemiológicos usando procesos de Poisson no homogéneos y datos funcionales
DOI:
https://doi.org/10.17533/udea.redin.20250367Palabras clave:
Función de Intensidad, Modelo Lineal Generalizado, Trayectorias Estocásticas, Estimación de Profundidad, Envolventes de ConfianzaResumen
En este trabajo, presentamos una nueva metodología para modelar variables de conteo discretas dentro del marco de procesos estocásticos. Nuestro enfoque integra dos áreas estadísticas: los procesos de Poisson no homogéneos para la estimación y predicción de funciones de intensidad basadas en variables explicativas y las técnicas de estimación de datos funcionales. A través de un estudio de caso integral centrado en una enfermedad infecciosa de características virales, demostramos el potencial de nuestra metodología. Proporcionamos evidencia empírica de que nuestra metodología ofrece una alternativa robusta para modelar variables de conteo. Nuestros hallazgos apoyan la utilidad de nuestro enfoque para capturar la dinámica compleja inherente a los datos de conteo en los fenómenos epidemiológicos de enfermedades infecciosas.
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