Necessary and sufficient conditions for the pointwise convergence of nearest neighbor regression function estimates
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Publication:3942221
DOI10.1007/BF00531618zbMath0483.62029OpenAlexW2006783977MaRDI QIDQ3942221
Publication date: 1982
Published in: Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf00531618
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Cites Work
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- Monte Carlo algorithms for optimal stopping and statistical learning
- A new approach to least-squares estimation, with applications
- Application of structural risk minimization to multivariate smoothing spline regression estimates
- Estimating a regression function
- Number of paths versus number of basis functions in American option pricing
- Optimal global rates of convergence for nonparametric regression
- A distribution-free theory of nonparametric regression
- A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options
- Nonparametric regression with additional measurement errors in the dependent variable
- PRICING OF HIGH-DIMENSIONAL AMERICAN OPTIONS BY NEURAL NETWORKS
- Necessary and sufficient conditions for the pointwise convergence of nearest neighbor regression function estimates
- Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives
- Nonparametric regression estimation using penalized least squares
- Estimating market risk with neural networks