Estimation of Conditional Quantile Using Neural Networks

Abstract

The problem of estimating conditional quantiles using neural networks is investigated here. A basic structure is developed using the methodology of kernel estimation, and a theory guaranteeing consistency on a mild set of assumptions is provided. The constructed structure constitutes a basis for the design of a variety of different neural networks, some of which are considered in detail. The task of estimating conditional quantiles is related to Bayes point estimation whereby a broad range of applications within engineering, economics and management can be suggested. Numerical results illustrating the capabilities of the elaborated neural network are also given.