Abstract
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 is proved from a mild set of assumptions. A number of applications within statistcs, decision theory and signal processing are suggested, and a numerical example illustrating the capabilities of the elaborated network is given
Original language | Danish |
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Publisher | <Forlag uden navn> |
Publication status | Published - 1997 |