### Abstract

Let {(X_{i}, Y_{i})} be a stationary ergodic R^{d}×R valued process. This study offers a strongly consistent (with respect to pointwise, least-squares, and uniform distance) algorithm for inferring the regression function E[Y_{0}|X_{0} = x], assumed uniformly Lipschitz continuous.

Original language | English |
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Number of pages | 1 |

Publication status | Published - Jan 1 1997 |

Event | Proceedings of the 1997 IEEE International Symposium on Information Theory - Ulm, Ger Duration: Jun 29 1997 → Jul 4 1997 |

### Other

Other | Proceedings of the 1997 IEEE International Symposium on Information Theory |
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City | Ulm, Ger |

Period | 6/29/97 → 7/4/97 |

### ASJC Scopus subject areas

- Theoretical Computer Science
- Information Systems
- Modelling and Simulation
- Applied Mathematics

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## Cite this

Yakowitz, S., Gyorfi, L., Kieffer, J., & Morvai, G. (1997).

*Strongly-consistent nonparametric estimation of smooth regression functions for stationary ergodic sequences*. Paper presented at Proceedings of the 1997 IEEE International Symposium on Information Theory, Ulm, Ger, .