Data-dependent kn/-NN estimators consistent for arbitrary processes

S. R. Kulkarni, S. E. Posner, S. Sandilya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Let X1 , X2,... be an arbitrary random process taking values in a totally bounded subset of a separable metric space. Associated with Xi we observe Yi drawn from an unknown conditional distribution F(y|Xi=x) with continuous regression function m(x)=E[Y|X=·x]. The problem of interest is to estimate Yn based on Xn and the data {(Xi,Yi)}i=1 n-1. We construct an appropriate data-dependent nearest neighbor estimator and show, with a very elementary proof, that it is consistent for every process X1,X2.

Original languageEnglish (US)
Title of host publicationProceedings - 1998 IEEE International Symposium on Information Theory, ISIT 1998
Number of pages1
DOIs
StatePublished - Dec 1 1998
Event1998 IEEE International Symposium on Information Theory, ISIT 1998 - Cambridge, MA, United States
Duration: Aug 16 1998Aug 21 1998

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095

Other

Other1998 IEEE International Symposium on Information Theory, ISIT 1998
CountryUnited States
CityCambridge, MA
Period8/16/988/21/98

All Science Journal Classification (ASJC) codes

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

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    Kulkarni, S. R., Posner, S. E., & Sandilya, S. (1998). Data-dependent kn/-NN estimators consistent for arbitrary processes. In Proceedings - 1998 IEEE International Symposium on Information Theory, ISIT 1998 [708993] (IEEE International Symposium on Information Theory - Proceedings). https://doi.org/10.1109/ISIT.1998.708993