• DocumentCode
    752069
  • Title

    Large-scale typicality of Markov sample paths and consistency of MDL order estimators

  • Author

    Csiszár, Imre

  • Author_Institution
    A. Renyi Inst. of Math., Hungarian Acad. of Sci., Budapest, Hungary
  • Volume
    48
  • Issue
    6
  • fYear
    2002
  • fDate
    6/1/2002 12:00:00 AM
  • Firstpage
    1616
  • Lastpage
    1628
  • Abstract
    For Markov chains of arbitrary order, with finite alphabet A, almost sure sense limit theorems are proved on relative frequencies of k-blocks, and of symbols preceded by a given k-block, when k is permitted to grow as the sample size n grows. As-an application, the-consistency of two kinds of minimum description length (MDL) Markov order estimators is proved, with upper bound o(log n), respectively, α log n with α < 1/log |A|, on the permissible value of the estimated order. It was shown by Csiszar and Shields (see Ann. Statist., vol.28, p.1601-1619, 2000) that in the absence of any bound, or with bound α log n with large α consistency fails
  • Keywords
    Markov processes; encoding; maximum likelihood estimation; Krichevsky-Trofimov distribution; MDL order estimators; Markov chains; Markov order estimators; Markov sample paths; almost sure sense limit theorems; codeword; consistency; finite alphabet; large-scale typicality; minimum description length; normalized maximum likelihood coding distribution; sample size; stochastic process; uniform distribution; upper bound; Estimation theory; Frequency estimation; H infinity control; Information theory; Large-scale systems; Mathematics; Maximum likelihood estimation; Stochastic processes; Upper bound;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/TIT.2002.1003842
  • Filename
    1003842