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Simone Borra

Personal Details

First Name:Simone
Middle Name:
Last Name:Borra
Suffix:
RePEc Short-ID:pbo1156

Affiliation

Facoltà di Economia
Università degli Studi di Roma "Tor Vergata"

Roma, Italy
https://economia.uniroma2.it/
RePEc:edi:ferotit (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. Borra, Simone & Di Ciaccio, Agostino, 2010. "Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2976-2989, December.
  2. 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.
  3. Simone Borra & Agostino Di Ciaccio, 2001. "Performance evaluation of Bagging and Boosting in nonparametric regression," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 141-156.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Borra, Simone & Di Ciaccio, Agostino, 2010. "Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2976-2989, December.

    Cited by:

    1. Mario Guevara & Rodrigo Vargas, 2019. "Downscaling satellite soil moisture using geomorphometry and machine learning," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-20, September.
    2. Nader Salari & Shamarina Shohaimi & Farid Najafi & Meenakshii Nallappan & Isthrinayagy Karishnarajah, 2014. "A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-50, November.
    3. Keunhyun Park & Sadegh Sabouri & Torrey Lyons & Guang Tian & Reid Ewing, 2020. "Intrazonal or interzonal? Improving intrazonal travel forecast in a four-step travel demand model," Transportation, Springer, vol. 47(5), pages 2087-2108, October.
    4. Ha, Tran Vinh & Asada, Takumi & Arimura, Mikiharu, 2019. "Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods," Journal of Transport Geography, Elsevier, vol. 78(C), pages 70-86.
    5. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    6. Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
    7. George Chalamandaris & Nikos E. Vlachogiannakis, 2018. "Are financial ratios relevant for trading credit risk? Evidence from the CDS market," Annals of Operations Research, Springer, vol. 266(1), pages 395-440, July.
    8. Adelman,Melissa Ann & Haimovich,Francisco & Ham,Andres & Vazquez,Emmanuel Jose, 2017. "Predicting school dropout with administrative data: new evidence from Guatemala and Honduras," Policy Research Working Paper Series 8142, The World Bank.
    9. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Conde, David & Fernández, Miguel & Salvador, Bonifacio & Rueda, Cristina, 2015. "dawai: An R Package for Discriminant Analysis with Additional Information," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i10).
    11. Christoph Bergmeir & Rob J Hyndman & Bonsoo Koo, 2015. "A Note on the Validity of Cross-Validation for Evaluating Time Series Prediction," Monash Econometrics and Business Statistics Working Papers 10/15, Monash University, Department of Econometrics and Business Statistics.
    12. Khaled Yousef Almansi & Abdul Rashid Mohamed Shariff & Bahareh Kalantar & Ahmad Fikri Abdullah & Sharifah Norkhadijah Syed Ismail & Naonori Ueda, 2022. "Performance Evaluation of Hospital Site Suitability Using Multilayer Perceptron (MLP) and Analytical Hierarchy Process (AHP) Models in Malacca, Malaysia," Sustainability, MDPI, vol. 14(7), pages 1-36, March.
    13. Bergmeir, Christoph & Costantini, Mauro & Benítez, José M., 2014. "On the usefulness of cross-validation for directional forecast evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 132-143.

  2. 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.

    Cited by:

    1. K.W. de Bock & K. Coussement & D. van den Poel, 2010. "Ensemble classification based on generalized additive models," Post-Print halshs-00581711, HAL.
    2. 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.
    3. 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.
    4. 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.
    5. Gey, Servane & Poggi, Jean-Michel, 2006. "Boosting and instability for regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 533-550, January.
    6. 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.
    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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