Strong universal pointwise consistency of some regression function estimates
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Publication:1808841
DOI10.1006/jmva.1999.1836zbMath0983.62023OpenAlexW1975429654MaRDI QIDQ1808841
Publication date: 25 April 2002
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1006/jmva.1999.1836
Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12)
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Cites Work
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- Distribution-free pointwise consistency of kernel regression estimate
- Strong convergence of kernel estimates of nonparametric regression functions
- On almost sure convergence of conditional empirical distribution functions
- Distribution-free consistency results in nonparametric discrimination and regression function estimation
- Consistent window estimation in nonparametric regression
- Consistency of a recursive nearest neighbor regression function estimate
- On the almost everywhere convergence of nonparametric regression function estimates
- Consistent nonparametric regression. Discussion
- An equivalence theorem for \(L_ 1\) convergence of the kernel regression estimate
- On a universal strong law of large numbers for conditional expectations
- Nearest neighbor regression estimation for null-recurrent Markov time series
- Strong universal pointwise consistency of recursive regression estimates
- On the strong universal consistency of nearest neighbor regression function estimates
- Nonparametric inference for ergodic, stationary time series
- Distribution-free consistency of a nonparametric kernel regression estimate and classification
- An elementary proof of the strong law of large numbers
- Necessary and sufficient conditions for the pointwise convergence of nearest neighbor regression function estimates
- Weakly convergent nonparametric forecasting of stationary time series
- Universal schemes for learning the best nonlinear predictor given the infinite past and side information
- The rates of convergence of kernel regression estimates and classification rules
- Estimation by the nearest neighbor rule