Skip to main content
Log in

A local convergence theorem for partial sums of stochastic adapted sequences

  • Published:
Czechoslovak Mathematical Journal Aims and scope Submit manuscript

Abstract

In this paper we establish a new local convergence theorem for partial sums of arbitrary stochastic adapted sequences. As corollaries, we generalize some recently obtained results and prove a limit theorem for the entropy density of an arbitrary information source, which is an extension of case of nonhomogeneous Markov chains.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. P. Algoat, T. Cover: A sandwich proof of the Shannon-McMillan-Breiman theorem. Annals of Probability 16 (1988), 899–909.

    MathSciNet  Google Scholar 

  2. W. Liu, J. A. Yan, and W. G. Yang: A limit theorem for partial sums of random variables and its applications. Statistics and Probability Letters 62 (2003), 79–86.

    Article  MATH  MathSciNet  Google Scholar 

  3. W. Liu: Some limit properties of the multivariate function sequences of discrete random variables. Statistics and Probability Letters 61 (2003), 41–50.

    Article  MATH  MathSciNet  Google Scholar 

  4. W. Liu, W. G. Yang: A limit theorem for the entropy density of nonhomogeneous Markov information source. Statistics and Probability Letters 22 (1995), 295–301.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, W., Ye, Z. & Liu, W. A local convergence theorem for partial sums of stochastic adapted sequences. Czech Math J 56, 525–532 (2006). https://doi.org/10.1007/s10587-006-0034-4

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10587-006-0034-4

Keywords

Navigation