Kernel density estimation from ergodic sample is not universally consistent

L. Györfi, Gábor Lugosi

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

We show that kernel density estimation under the usual conditions does not converge necessarily in L1 if the sample is ergodic.

Original languageEnglish
Pages (from-to)437-442
Number of pages6
JournalComputational Statistics and Data Analysis
Volume14
Issue number4
DOIs
Publication statusPublished - 1992

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Kernel Density Estimation
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Kernel density estimation

Keywords

  • Density estimation
  • Ergodic observation
  • Kernel estimate
  • Optimization
  • Rotation process

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Numerical Analysis
  • Statistics and Probability

Cite this

Kernel density estimation from ergodic sample is not universally consistent. / Györfi, L.; Lugosi, Gábor.

In: Computational Statistics and Data Analysis, Vol. 14, No. 4, 1992, p. 437-442.

Research output: Contribution to journalArticle

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