# Neural Networks for Partially Linear Quantile Regression

@article{Zhong2021NeuralNF, title={Neural Networks for Partially Linear Quantile Regression}, author={Qixian Zhong and Jane-ling Wang}, journal={arXiv: Statistics Theory}, year={2021} }

Deep learning has enjoyed tremendous success in a variety of applications but its application to quantile regressions remains scarce. A major advantage of the deep learning approach is its flexibility to model complex data in a more parsimonious way than nonparametric smoothing methods. However, while deep learning brought breakthroughs in prediction, it often lacks interpretability due to the black-box nature of multilayer structure with millions of parameters, hence it is not well suited for…

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