• DocumentCode
    1367171
  • Title

    About the asymptotic accuracy of Barron density estimates

  • Author

    Berlinet, Alain ; Vajda, Igor ; Van der Meulen, Edward C.

  • Author_Institution
    Dept. of Stat., Univ. des Sci. et Tech. du Languedoc, Montpellier, France
  • Volume
    44
  • Issue
    3
  • fYear
    1998
  • fDate
    5/1/1998 12:00:00 AM
  • Firstpage
    999
  • Lastpage
    1009
  • Abstract
    By extending the information-theoretic arguments of previous papers dealing with the Barron-type density estimates, and their consistency in information divergence and chi-square divergence, the problem of consistency in Csiszar´s φ-divergence is motivated for general convex functions φ. The problem of consistency in φ-divergence is solved for all φ with φ(0)<∞ and φ(t)=O(t ln t) when t→∞. The problem of consistency in the expected φ-divergence is solved for all φ with tφ(1/t)+φ(t)=O(t2) when t→∞. Various stronger versions of these asymptotic restrictions are considered too. Assumptions about the model needed for the consistency are shown to depend on how strong these restrictions are
  • Keywords
    estimation theory; information theory; nonparametric statistics; Barron density estimates; Csiszar´s φ-divergence; Renyi distances; asymptotic accuracy; chi-square divergence; general convex functions; information divergence; information-theoretic arguments; nonparametric density estimates; Automation; Histograms; Information theory; Kernel; Mathematics; Packaging; Statistics; Topology;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/18.669143
  • Filename
    669143