Publication: Propuesta de tarificación para un producto con cobertura gap en el sector agro, a partir de datos abiertos en el sector colombiano caso Antioquia
Files
Authors
Abstract (Spanish)
Abstract (English)
Director
Advisors/Directors
Extent
Collections
References
Abrego-Perez, A. L., Pacheco-Carvajal, N., & Diaz-Jimenez, M. C. (2023). Forecasting agricultural financial weather risk using PCA and SSA in an index insurance model in low-income economies. Applied Sciences, 13(4), 2425. https://doi.org/10.3390/app13042425
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). ACM.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.
Datos Abiertos Colombia. (s.f.). Áreas cultivadas y producción agrícola en Antioquia desde 1990–2022. Recuperado de https://www.datos.gov.co
Diop, A. N. (2019). Agricultural risk pricing in Senegal. Journal of Mathematical Finance, 9(2), 182–201. https://doi.org/10.4236/jmf.2019.92010
Finan, M. B. (2020). A probability course for the actuaries: A preparation for Exam P/1 (Revised ed.). Arkansas Tech University.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63, 3–42.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Hazell, P., Pomareda, C., & Valdés, A. (1986). Crop Insurance for Agricultural Development: Issues and Experience. World Bank Publications.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
Just & Weninger (1999). Are Crop Yields Normally Distributed? American Journal of Agricultural Economics, 81(2): 287–304.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 3146–3154).
Ministerio de Agricultura y Desarrollo Rural. (2025, 17 de febrero). El sector Agricultura, protagonista en 2024 de la reactivación económica del país. https://www.minagricultura.gov.co/noticias/Paginas/El-sector-Agricultura,protagonista-en-2024-de-la-reactivaci%C3%B3n-econ%C3%B3mica-delpa%C3%ADs.aspx
Powers, D. M. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. Journal of Machine Learning Technologies, 2(1), 37–63.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.
Skees, J., Hazell, P., & Miranda, M. (1999). New Approaches to Public/Private Crop Yield Insurance. World Bank Publications.
Stephens, M. A. (1986). Tests Based on EDF Statistics. In Goodness-of-Fit Techniques, D’Agostino & Stephens (Eds.)
Thode, H. (2002). Testing for Normality. CRC Press. (Cap. 3)
Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320.