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 con-sistency 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.
Original language | Danish |
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Journal | Periodica Polytechnica |
Pages (from-to) | 109-126 |
Publication status | Published - 1999 |