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Learning Multiple Quantiles With Neural Networks with preserving monotonicity among quantiles

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l1-penalizing method

  • Here is the code implementing the l1-penalizing method introduced in Learning Multiple Quantiles With Neural Networks.
  • The algorithm provides non-crossing quantiles estimates of response variable for given explanatory data.
  • Additionally, projected gradient descent method and interior-point method for multiple quantiles are also included.

Cite

@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}
}

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Learning Multiple Quantiles With Neural Networks with preserving monotonicity among quantiles

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