A. Medialdea Villanueva, J. M. Angulo, J. Mateu

Los procesos de Cox log-gaussianos constituyen una clase flexible de modelos espacio-temporales que permiten representar una amplia variedad de efectos de dependencia en patrones puntuales. En este contexto, partiendo de un proceso puntual espacio-temporal se han estimado las características de primer y segundo orden del proceso con el objetivo de obtener un modelo que permita predecir eventos futuros en el espacio. La estimación de la función de intensidad de primer orden se ha realizado bajo los enfoques separable y no separable de las dimensiones espacial y temporal, mientras que la estimación de la componente aleatoria del modelo se ha obtenido en ambos casos a través de una función de covarianza separable mediante un procedimiento de simulación condicionada. El desempeño de ambos modelos se ha comparado mediante medidas de información y complejidad que permiten cuantificar el grado de transferencia estructural entre la intensidad y el patrón de puntos resultante para cada modelo.

Keywords: complejidad, entropía, modelo de Cox log-gaussiano, proceso puntual espacio-temporal


Spatial Statistics and Temporal Space II
June 7, 2022  4:50 PM
Conference hall

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