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Entropía de transferencia para datos finitos
1University of Hong Kong, University of Hong Kong, University of Hong Kong, Institute of Data Science, Hong Kong SAR, China; Department of Urban Planning and Design, Hong Kong SAR, China; and Urban Systems Institute, Hong Kong SAR, China.
Physical review. E
|December 23, 2025
Resumen
Este estudio presenta una nueva medida de entropía de transferencia para datos discretos, superando problemas de sesgo y significancia en el análisis de series temporales pequeñas o de alta cardinalidad. Permite una evaluación fiable del flujo de información sin simulaciones.
Área de la Ciencia:
- Análisis de sistemas complejos
- Teoría de la información
- Análisis de series temporales
Sus antecedentes:
- La entropía de transferencia cuantifica el flujo de información dirigido, pero enfrenta desafíos con datos continuos.
- Para datos discretos, sufre de sesgo positivo con recuentos dispersos y carece de evaluación de significancia estadística.
- Los métodos existentes luchan con flujos de datos finitos de tamaño pequeño o alta cardinalidad.
Objetivo del estudio:
- Desarrollar una nueva medida de entropía de transferencia para flujos de datos discretos finitos.
- Abordar las limitaciones de los estimadores existentes, específicamente el sesgo y la falta de prueba de significancia.
- Permitir la evaluación de significancia estadística no paramétrica sin depender de simulaciones.
Principales métodos:
- Se derivó una nueva medida de entropía de transferencia calculando el contenido de información en flujos de datos finitos.
- Se evitó la consideración explícita de símbolos como variables aleatorias.
- Se aseguró la equivalencia asintótica al estimador estándar de "plug-in".
Principales resultados:
- La nueva medida es asintóticamente equivalente al estimador estándar de "plug-in".
- Remedia eficazmente los problemas de sesgo positivo asociados con los recuentos de bin dispersos.
- Permite una evaluación totalmente no paramétrica de la significancia estadística para series temporales finitas.
- El método es adecuado para series temporales de tamaño pequeño y/o alta cardinalidad.
Conclusiones:
- La medida de entropía de transferencia propuesta ofrece una solución robusta para analizar el flujo de información en datos discretos finitos.
- Supera las limitaciones críticas de los métodos tradicionales, mejorando la fiabilidad y la interpretabilidad.
- Permite la validación estadística rigurosa de la transferencia de información dirigida en sistemas complejos.
Palabras clave:
entropía de transferenciaseries temporales discretasanálisis de sistemas complejosteoría de la informaciónanálisis de series temporalessesgo de estimaciónprueba de significancia estadísticaMás Videos Relacionados
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