Strongly-consistent nonparametric estimation of smooth regression functions for stationary ergodic sequences

Sidney Yakowitz, Laszlo Gyorfi, John Kieffer, Gusztav Morvai

Research output: Contribution to conferencePaper

Abstract

Let {(Xi, Yi)} be a stationary ergodic Rd×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[Y0|X0 = x], assumed uniformly Lipschitz continuous.

Original languageEnglish
Number of pages1
Publication statusPublished - Jan 1 1997
EventProceedings of the 1997 IEEE International Symposium on Information Theory - Ulm, Ger
Duration: Jun 29 1997Jul 4 1997

Other

OtherProceedings of the 1997 IEEE International Symposium on Information Theory
CityUlm, Ger
Period6/29/977/4/97

ASJC Scopus subject areas

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

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    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, .