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.
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© 1992 Springer-Verlag New York, Inc.
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White, H. (1992). Nonparametric Estimation of Conditional Quantiles Using Neural Networks. In: Page, C., LePage, R. (eds) Computing Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2856-1_25
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DOI: https://doi.org/10.1007/978-1-4612-2856-1_25
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-97719-5
Online ISBN: 978-1-4612-2856-1
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