Kernel density estimation from ergodic sample is not universally consistent

László 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 - Nov 1992

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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