Una adaptación de las máquinas de soporte vectorial para la estimación de eficiencia técnica en un contexto multi-respuesta
D. Valero Carreras, J. Aparicio Baeza, N. M. Guerrero
Las Máquinas de Soporte Vectorial de Regresión (SVR) han sido utilizadas para la estimación de fronteras a través de la técnica Support Vector Frontiers (SVF). Esta nueva metodología permite abarcar esta problemática frente a otros métodos tradicionales como Free Disposal Hull (FDH) o Data Envelopment Analysis (DEA). En este trabajo se amplía SVF para adaptarlo a escenarios con múltiples respuestas demostrando que DEA y FDH pueden ser vistos como escenarios particulares de SVF. Además, se introduce un nuevo concepto de eficiencia relacionada con el concepto de ɛ-insensibilidad. Debido a razones computacionales, se ha introducido una versión simplificada del modelo SVF multioutput que ha sido validada mediante experiencias simuladas. Estas experiencias demuestran que el nuevo enfoque mejora a FDH y DEA tanto en error cuadrático medio como en sesgo. Finalmente, se ha realizado un ejemplo empírico en el que se calculan diferentes eficiencias utilizando para ello una base de datos real.
Keywords: Análisis Envolvente de Datos, Máquinas de Soporte Vectorial; Eficiencia; Experiencia computacional
Scheduled
Data Envelopment Analysis I
June 10, 2022 10:10 AM
Conference hall
Other papers in the same session
M. Kapelko, J. Aparicio Baeza, L. Ortiz Henarejos
M. C. Sánchez Gil, M. Arana Jiménez, S. Lozano Segura, A. Younsesi
J. F. Monge Ivars, J. Aparicio Baeza
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