C. Giner Baixauli, J. T. Rodríguez González, A. Álvaro Meca, D. Vélez Serrano

Existen situaciones en las que la capacidad predictiva de una técnica analítica no es suficiente para tomar decisiones, siendo necesario justificar bien que las estimaciones que proporciona responden a una lógica entendible. En este contexto, los modelos de tipo árbol gozan de popularidad por su interpretabilidad. Además, aquellos que basan el proceso de segmentación en test de hipótesis, suelen tener mayor capacidad explicativa. Por otra parte, son también habituales los problemas en los que existen varias variables objetivo en función de las cuales tomar dichas decisiones.
Se plantea un método novedoso para ajustar árboles de decisión multitarget basando el proceso de segmentación en test MANOVA y se aplica para explicar simultáneamente las tasas de infectados y fallecidos por COVID19 en función de variables sociodemográficas medidas en municipios andaluces. Dicho conocimiento puede ser relevante de cara a priorizar ciertos perfiles poblacionales durante el proceso de vacunación.

Keywords: Árboles de decisión, multitatget, MANOVA, COVID19


Statistical Models I
June 10, 2022  10:10 AM

Other papers in the same session

Small area estimation of average compositions under multivariate nested error regression models

E. López vizcaíno, D. Morales, M. J. Lombardía Cortiña, A. Pérez Martín, M. D. Esteban Lefler

Extended half-power exponential distribution with applications

K. I. Santoro, H. J. Gómez, I. Barranco Chamorro, H. W. Gómez

Empirical best prediction of small area bivariate parameters

D. Morales, M. D. Esteban, M. J. Lombardía, E. López-Vizcaíno, A. Pérez

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