https://orcid.org/0000-0002-4660-4542
References
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
- Brooks, T. F., Pope, D. S., & Marcolini, M. A. (1989). Airfoil self-noise and prediction [Tech. Rep]. NASA.
- 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). Association for Computing Machinery.
- Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266–298. https://doi.org/10.1214/09-AOAS285
- Dua, D., & Graff, C. (2017). UCI machine learning repository. https://archive.ics.uci.edu/ml
- Lin, W., Shi, Z., Wang, Y., & Yan, T. H. (2023). Unfolding Beijing in a hedonic way. Computational Economics, 61(1), 1–24. https://doi.org/10.1007/s10614-021-10209-3
- Linero, A. R. (2018). Bayesian regression trees for high-dimensional prediction and variable selection. Journal of the American Statistical Association, 113(522), 626–636. https://doi.org/10.1080/01621459.2016.1264957
- Linero, A. R., & Yang, Y. (2018). Bayesian regression tree ensembles that adapt to smoothness and sparsity. Journal of the Royal Statistical Society Series B: Statistical Methodology, 80(5), 1087–1110. https://doi.org/10.1111/rssb.12293
- Tan, Y. V., & Roy, J. (2019). Bayesian additive regression trees and the general BART model. Statistics in Medicine, 38(25), 5048–5069. https://doi.org/10.1002/sim.v38.25