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Nonparametric Estimation of Conditional Quantiles Using Neural Networks

In: Computing Science and Statistics

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  • Halbert White

    (University of California, Department of Economics and Institute for Neural Computation)

Abstract

We establish the consistency of nonparametric conditional quantile estimators based on artificial neural networks. The results follow from general results on sieve estimation for dependent processes. We also show that conditional quantiles can be learned to any pre-specified accuracy using approximate rather than exact network optimization.

Suggested Citation

  • Halbert White, 1992. "Nonparametric Estimation of Conditional Quantiles Using Neural Networks," Springer Books, in: Connie Page & Raoul LePage (ed.), Computing Science and Statistics, pages 190-199, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4612-2856-1_25
    DOI: 10.1007/978-1-4612-2856-1_25
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