Una adaptación de MARS para la estimación de funciones de producción en el contexto del Análisis Envolvente de Datos
V. J. España Roch, J. Aparicio Baeza, X. Barber Vallés, M. Esteve Campello
El artículo que se presenta introduce una nueva metodología para la estimación de funciones de producción que satisface axiomas clásicos de la teoría de producción (monotonía y concavidad) a través de la adaptación de la técnica de aprendizaje automático Multivariate Adaptive Regression Splines (MARS). Este nuevo enfoque, bautizado como Multivariate Adaptive Frontier Splines (MAFS), comparte con el Análisis Envolvente de Datos (DEA) la forma del predictor como una función lineal a trozos. Sin embargo, MAFS supera los problemas de sobreajuste presentes en DEA al recurrir a la técnica de validación cruzada generalizada. En este artículo se ha empleado un experimento computacional para medir el desempeño de MAFS, demostrando que este nuevo enfoque reduce significativamente el error cuadrático medio y el sesgo del estimador de la verdadera función de producción en comparación con DEA y la técnica de Corrected Concave Non-Parametric Least Squares (C2NLS).
Keywords: Análisis Envolvente de Datos, Multivariate Adaptive Regression Splines, aprendizaje automático, sobreajuste.
Scheduled
Data Envelopment Analysis II
June 10, 2022 4:00 PM
Conference hall
Other papers in the same session
M. D. Guillén, J. Aparicio Baeza, M. Esteve Campello
F. Fernández Palacín, M. A. López Sánchez, M. Muñoz Márquez
R. Moragues, J. Aparicio, M. Esteve
M. Arana Jiménez, A. Younesi, M. C. Sánchez Gil, S. Lozano Segura
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