IDEAS home Printed from https://ideas.repec.org/a/eee/spapps/v179y2025ics0304414924002199.html
   My bibliography  Save this article

Compound Poisson distributions for random dynamical systems using probabilistic approximations

Author

Listed:
  • Amorim, Lucas
  • Haydn, Nicolai
  • Vaienti, Sandro

Abstract

We obtain quenched hitting distributions to be compound Poissonian for a certain class of random dynamical systems. The theory is general and designed to accommodate non-uniformly expanding behavior and targets that do not overlap much with the region where uniformity breaks. Based on annealed and quenched polynomial decay of correlations, our quenched result adopts annealed Kac-type time-normalization and finds limits to be noise-independent. The technique involves a probabilistic block-approximation where the quenched hit-counting function up to annealed Kac-normalized time is split into equally sized blocks which are mimicked by an independency of random variables distributed just like each of them. The theory is made operational due to a result that allows certain hitting quantities to be recovered from return quantities. Our application is to a class of random piecewise expanding one-dimensional systems, casting new light on the well-known deterministic dichotomy between periodic and aperiodic points, their usual extremal index formula EI=1−1/JTp(x0), and recovering the Polya–Aeppli case for general Bernoulli-driven systems, but distinct behavior otherwise.

Suggested Citation

  • Amorim, Lucas & Haydn, Nicolai & Vaienti, Sandro, 2025. "Compound Poisson distributions for random dynamical systems using probabilistic approximations," Stochastic Processes and their Applications, Elsevier, vol. 179(C).
  • Handle: RePEc:eee:spapps:v:179:y:2025:i:c:s0304414924002199
    DOI: 10.1016/j.spa.2024.104511
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304414924002199
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.spa.2024.104511?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.

    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:spapps:v:179:y:2025:i:c:s0304414924002199. 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.

    We have no bibliographic references for this item. You can help adding them by using 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/505572/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.