Estimating conditional distributions by neural networks

Neural networks for estimating conditional distributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency property is considered from a mild set of assumptions. A number of applications within statistics, decision theory and signal processing are suggested.