Exponential inequalities for VLMC empirical trees

Antonio Galves; Véronique Maume-Deschamps; Bernard Schmitt

ESAIM: Probability and Statistics (2008)

  • Volume: 12, page 219-229
  • ISSN: 1292-8100

Abstract

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A seminal paper by Rissanen, published in 1983, introduced the class of Variable Length Markov Chains and the algorithm Context which estimates the probabilistic tree generating the chain. Even if the subject was recently considered in several papers, the central question of the rate of convergence of the algorithm remained open. This is the question we address here. We provide an exponential upper bound for the probability of incorrect estimation of the probabilistic tree, as a function of the size of the sample. As a consequence we prove the almost sure consistency of the algorithm Context. We also derive exponential upper bounds for type I errors and for the probability of underestimation of the context tree. The constants appearing in the bounds are all explicit and obtained in a constructive way.

How to cite

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Galves, Antonio, Maume-Deschamps, Véronique, and Schmitt, Bernard. "Exponential inequalities for VLMC empirical trees." ESAIM: Probability and Statistics 12 (2008): 219-229. <http://eudml.org/doc/250385>.

@article{Galves2008,
abstract = { A seminal paper by Rissanen, published in 1983, introduced the class of Variable Length Markov Chains and the algorithm Context which estimates the probabilistic tree generating the chain. Even if the subject was recently considered in several papers, the central question of the rate of convergence of the algorithm remained open. This is the question we address here. We provide an exponential upper bound for the probability of incorrect estimation of the probabilistic tree, as a function of the size of the sample. As a consequence we prove the almost sure consistency of the algorithm Context. We also derive exponential upper bounds for type I errors and for the probability of underestimation of the context tree. The constants appearing in the bounds are all explicit and obtained in a constructive way. },
author = {Galves, Antonio, Maume-Deschamps, Véronique, Schmitt, Bernard},
journal = {ESAIM: Probability and Statistics},
keywords = {Variable Length Markov Chain; context tree; algorithm context; weak dependance; variable length Markov chain},
language = {eng},
month = {1},
pages = {219-229},
publisher = {EDP Sciences},
title = {Exponential inequalities for VLMC empirical trees},
url = {http://eudml.org/doc/250385},
volume = {12},
year = {2008},
}

TY - JOUR
AU - Galves, Antonio
AU - Maume-Deschamps, Véronique
AU - Schmitt, Bernard
TI - Exponential inequalities for VLMC empirical trees
JO - ESAIM: Probability and Statistics
DA - 2008/1//
PB - EDP Sciences
VL - 12
SP - 219
EP - 229
AB - A seminal paper by Rissanen, published in 1983, introduced the class of Variable Length Markov Chains and the algorithm Context which estimates the probabilistic tree generating the chain. Even if the subject was recently considered in several papers, the central question of the rate of convergence of the algorithm remained open. This is the question we address here. We provide an exponential upper bound for the probability of incorrect estimation of the probabilistic tree, as a function of the size of the sample. As a consequence we prove the almost sure consistency of the algorithm Context. We also derive exponential upper bounds for type I errors and for the probability of underestimation of the context tree. The constants appearing in the bounds are all explicit and obtained in a constructive way.
LA - eng
KW - Variable Length Markov Chain; context tree; algorithm context; weak dependance; variable length Markov chain
UR - http://eudml.org/doc/250385
ER -

References

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  9. F. Leonardi and A. Galves, Sequence Motif identification and protein classification using probabilistic trees. Lect. Notes Comput. Sci.3594 (2005) 190–193.  
  10. V. Maume-Deschamps, Exponential inequalities and estimation of conditional probabilities in Dependence in probability and statistics, Lect. Notes in Stat., Vol. 187, P. Bertail, P. Doukhan and P. Soulier Eds. Springer (2006).  Zbl1177.60025
  11. J. Rissanen, A universal data compression system. IEEE Trans. Inform. Theory29 (1983) 656–664.  Zbl0521.94010
  12. T.J. Tjalkens and F.M.J.F. Willems, Implementing the context-tree weighting method: arithmetic coding. Recent advances in interdisciplinary mathematics (Portland, ME, 1997). J. Combin. Inform. System Sci.25 (2000) 49-58.  Zbl1219.94071
  13. F.M. Willems, Y.M. Shtarkov and T.J Tjalkens, The context-tree weighting method: basic properties. IEEE Trans. Inform. Theory41 (1995) 653–664.  Zbl0837.94011

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