IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v80y2010i5-6p489-496.html
   My bibliography  Save this article

Remarks on the SLLN for linear random fields

Author

Listed:
  • Banys, Povilas
  • Davydov, Youri
  • Paulauskas, Vygantas

Abstract

We consider random linear fields on generated by ergodic or mixing (in particular case, independent identically distributed (i.i.d.)) random variables. Our main results generalize the classical Strong Law of Large Numbers (SLLN) for multi-indexed sums of i.i.d. random variables. These results are easily obtained using ergodic theory. Also we compare the results for SLLN obtained using ergodic theory and with the help of the Beveridge-Nelson decomposition.

Suggested Citation

  • Banys, Povilas & Davydov, Youri & Paulauskas, Vygantas, 2010. "Remarks on the SLLN for linear random fields," Statistics & Probability Letters, Elsevier, vol. 80(5-6), pages 489-496, March.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:5-6:p:489-496
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(09)00451-9
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Rieders, Eric, 1993. "The size of the averages of strongly mixing random variables," Statistics & Probability Letters, Elsevier, vol. 18(1), pages 57-64, August.
    2. Marinucci, D. & Poghosyan, S., 2001. "Asymptotics for linear random fields," Statistics & Probability Letters, Elsevier, vol. 51(2), pages 131-141, January.
    Full references (including those not matched with items on IDEAS)

    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. Kim, Tae-Sung & Ko, Mi-Hwa & Choi, Yong-Kab, 2008. "The invariance principle for linear multi-parameter stochastic processes generated by associated fields," Statistics & Probability Letters, Elsevier, vol. 78(18), pages 3298-3303, December.
    2. Paulauskas, Vygantas, 2010. "On Beveridge-Nelson decomposition and limit theorems for linear random fields," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 621-639, March.
    3. Santanu Dutta & Tushar Kanti Powdel, 2023. "Modeling Long Term Return Distribution and Nonparametric Market Risk Estimation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 257-289, May.

    More about this item

    Statistics

    Access and download statistics

    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:80:y:2010:i:5-6:p:489-496. 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.