A (robust) interquantile multilayer perceptron. The idea is to compute two selected nonlinear regression quantiles by means of multilayer perceptrons (MLPs) and the final regression fit is obtained as a standard MLP computed however only for such observations, which are between the two quantiles; to achieve robustness, the remaining observations are ignored completely.
Feel free to use or modify the code.
You need to install Python, its library NumPy, its math module, TensorFlow, and Keras (which itself is an open-source library written in Python).
- The usage of the code is straightforward. The training of the robust MLP is called in the same way as habitually used calling of a standard (non-robust) MLP.
- Jan Tichavský, The Czech Academy of Sciences, Institute of Computer Science
- Jan Kalina, The Czech Academy of Sciences, Institute of Computer Science
Do not hesitate to contact us (tichavsk@seznam.cz) or write an Issue.
When refering to the IQ-MLP method, please consider citing the following:
Kalina J, Vidnerová P (2020): On robust training of regression neural networks. In Aneiros G, Horová I, Hušková M, Vieu P (eds): Functional and High-Dimensional Statistics and Related Fields. IWFOS 2020, Contributions to Statistics. Springer, Cham, pp. 145-152.
This work was supported by the Czech Science Foundation grant GA19-05704S.