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Improving nonparametric regression methods by bagging and boosting

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  • Borra, Simone
  • Di Ciaccio, Agostino

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  • Borra, Simone & Di Ciaccio, Agostino, 2002. "Improving nonparametric regression methods by bagging and boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 407-420, February.
    • Handle: RePEc:eee:csdana:v:38:y:2002:i:4:p:407-420
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    Cited by:

    1. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
    2. Gey, Servane & Poggi, Jean-Michel, 2006. "Boosting and instability for regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 533-550, January.
    3. Yuanbing Zheng & Caixin Sun & Jian Li & Qing Yang & Weigen Chen, 2011. "Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data," Energies, MDPI, vol. 4(8), pages 1-10, August.
    4. Petersen, Maya L. & Molinaro, Annette M. & Sinisi, Sandra E. & van der Laan, Mark J., 2007. "Cross-validated bagged learning," Journal of Multivariate Analysis, Elsevier, vol. 98(9), pages 1693-1704, October.
    5. Rueda, Cristina, 2013. "Degrees of freedom and model selection in semiparametric additive monotone regression," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 88-99.
    6. Zhao, Shan & Wei, G. W., 2003. "Jump process for the trend estimation of time series," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 219-241, February.
    7. Haidong Huang & Zhixiong Zhang & Fengxuan Song, 2021. "An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1757-1773, April.

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