Modeling of Distributions with Neural Approximation of Conditional Quantiles

P. Wawrzynski and A. Pacut (Poland)


quantile regression, neural networks, datamining, modeling, discretization.


We propose a method of recurrent estimation of conditional quantiles stemming from stochastic approximation. The method employs a sigmoidal neural network and specialized training algorithm to approximate the conditional quantiles. The approach may by used in a wide range of fields, in partricular in econometrics, medicine, data mining, and modeling.

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