Nonparametric regression estimation for arbitrary random processes

S. E. Posner, S. R. Kulkarni

Research output: Contribution to conferencePaperpeer-review

Abstract

We study nonparametric estimates of E[Yn|Xn] of the form ∑i=1n-1 Wni(X1,..., Xn)Yi based on Xn and data {(Xi, Yi)}i=1n-1. Our work analyzes the case where {Xi} is a completely arbitrary random process. Conditions on the weights are established so that the time-average of the estimation errors converges to zero. One consequence of our work is a recovery and extension of some classical results to stationary processes in separable metric spaces.

Original languageEnglish (US)
Number of pages1
StatePublished - Jan 1 1995
EventProceedings of the 1995 IEEE International Symposium on Information Theory - Whistler, BC, Can
Duration: Sep 17 1995Sep 22 1995

Other

OtherProceedings of the 1995 IEEE International Symposium on Information Theory
CityWhistler, BC, Can
Period9/17/959/22/95

All Science Journal Classification (ASJC) codes

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

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