The l1-penalizing method (l1pm) provides non-crossing quantiles estimates of the response variable for given explanatory data. It ensures valid multiple quantile predictions by leveraging neural networks with a specialized lasso penalty approach.

Citation

If you use l1pm in your research or project, please cite it as follows:

@article{moon2021learning,
  title={Learning multiple quantiles with neural networks},
  author={Moon, Sang Jun and Jeon, Jong-June and Lee, Jason Sang Hun and Kim, Yongdai},
  journal={Journal of Computational and Graphical Statistics},
  volume={30},
  number={4},
  pages={1238--1248},
  year={2021},
  publisher={Taylor \& Francis}
}