IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v81y2011i12p1883-1890.html
   My bibliography  Save this article

The strong law of large numbers and the Shannon–McMillan theorem for nonhomogeneous Markov chains indexed by a Cayley tree

Author

Listed:
  • Dong, Yan
  • Yang, Weiguo
  • Bai, Jianfang

Abstract

In this paper, we study the strong law of large numbers and the Shannon–McMillan theorem for nonhomogeneous Markov chains indexed by a Cayley tree. This article generalizes the relative results of level nonhomogeneous Markov chains indexed by a Cayley tree.

Suggested Citation

  • Dong, Yan & Yang, Weiguo & Bai, Jianfang, 2011. "The strong law of large numbers and the Shannon–McMillan theorem for nonhomogeneous Markov chains indexed by a Cayley tree," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1883-1890.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:12:p:1883-1890
    DOI: 10.1016/j.spl.2011.06.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715211002318
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2011.06.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Wen & Yang, Weiguo, 2000. "The Markov approximation of the sequences of N-valued random variables and a class of small deviation theorems," Stochastic Processes and their Applications, Elsevier, vol. 89(1), pages 117-130, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhiyan Shi & Zhongzhi Wang & Pingping Zhong & Yan Fan, 2022. "The Generalized Entropy Ergodic Theorem for Nonhomogeneous Bifurcating Markov Chains Indexed by a Binary Tree," Journal of Theoretical Probability, Springer, vol. 35(3), pages 1367-1390, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Wen & Wang, Liying, 2003. "The Markov approximation of the random fields on Cayley trees and a class of small deviation theorems," Statistics & Probability Letters, Elsevier, vol. 63(2), pages 113-121, June.
    2. Yang, Weiguo, 2003. "Some limit properties for Markov chains indexed by a homogeneous tree," Statistics & Probability Letters, Elsevier, vol. 65(3), pages 241-250, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:81:y:2011:i:12:p:1883-1890. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.