Density estimation from an individual numerical sequence

Andrew B. Nobel, Gusztáv Morvai, Sanjeev R. Kulkarni

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

This paper considers estimation of a univariate density from an individual numerical sequence. It is assumed that 1) the limiting relative frequencies of the numerical sequence are governed by an unknown density, and 2) there is a known upper bound for the variation of the density on an increasing sequence of intervals. A simple estimation scheme is proposed, and is shown to be L 1 consistent when 1) and 2) apply. In addition, it is shown that there is no consistent estimation scheme for the set of individual sequences satisfying only condition 1).

Original languageEnglish
Pages (from-to)537-541
Number of pages5
JournalIEEE Transactions on Information Theory
Volume44
Issue number2
DOIs
Publication statusPublished - Dec 1 1998

Keywords

  • Bounded variation
  • Density estimation
  • Ergodic processes
  • Individual sequences

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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