IDEAS home Printed from https://ideas.repec.org/a/eee/spapps/v130y2020i10p5940-5972.html
   My bibliography  Save this article

Dirichlet forms and polymer models based on stable processes

Author

Listed:
  • Li, Liping
  • Li, Xiaodan

Abstract

In this paper, we are concerned with polymer models based on α-stable processes, where α∈(d2,d∧2) and d stands for dimension. They are attached with a delta potential at the origin and the associated Gibbs measures are parametrized by a constant γ∈R∪{−∞} playing the role of inverse temperature. Phase transition exhibits with critical value γcr=0. Our first object is to formulate the associated Dirichlet form of the canonical Markov process X(γ) induced by the Gibbs measure for a globular state γ>0 or the critical state γ=0. Approach of Dirichlet forms also leads to deeper descriptions of their probabilistic counterparts. Furthermore, we will characterize the behaviour of polymer near the critical point from probabilistic viewpoint by showing that X(γ) is convergent to X(0) as γ↓0 in a certain meaning.

Suggested Citation

  • Li, Liping & Li, Xiaodan, 2020. "Dirichlet forms and polymer models based on stable processes," Stochastic Processes and their Applications, Elsevier, vol. 130(10), pages 5940-5972.
  • Handle: RePEc:eee:spapps:v:130:y:2020:i:10:p:5940-5972
    DOI: 10.1016/j.spa.2020.04.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304414919303436
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.spa.2020.04.011?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. Kim, Panki, 2006. "Weak convergence of censored and reflected stable processes," Stochastic Processes and their Applications, Elsevier, vol. 116(12), pages 1792-1814, December.
    2. Blount, Douglas & Kouritzin, Michael A., 2010. "On convergence determining and separating classes of functions," Stochastic Processes and their Applications, Elsevier, vol. 120(10), pages 1898-1907, September.
    3. Chen, Zhen-Qing & Fukushima, Masatoshi, 2015. "One-point reflection," Stochastic Processes and their Applications, Elsevier, vol. 125(4), pages 1368-1393.
    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. Chen, Zhen-Qing & Peng, Jun, 2018. "Markov processes with darning and their approximations," Stochastic Processes and their Applications, Elsevier, vol. 128(9), pages 3030-3053.
    2. Iro Ren'e Kouarfate & Michael A. Kouritzin & Anne MacKay, 2020. "Explicit solution simulation method for the 3/2 model," Papers 2009.09058, arXiv.org, revised Jan 2021.
    3. Chen, Zhen-Qing & Fukushima, Masatoshi, 2018. "Stochastic Komatu–Loewner evolutions and BMD domain constant," Stochastic Processes and their Applications, Elsevier, vol. 128(2), pages 545-594.
    4. Kouritzin, Michael A. & Lê, Khoa & Sezer, Deniz, 2019. "Laws of large numbers for supercritical branching Gaussian processes," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3463-3498.
    5. Noba, Kei & Yano, Kouji, 2019. "Generalized refracted Lévy process and its application to exit problem," Stochastic Processes and their Applications, Elsevier, vol. 129(5), pages 1697-1725.
    6. Alonso Ruiz, Patricia, 2021. "Heat kernel analysis on diamond fractals," Stochastic Processes and their Applications, Elsevier, vol. 131(C), pages 51-72.
    7. Kouritzin, Michael A. & Ren, Yan-Xia, 2014. "A strong law of large numbers for super-stable processes," Stochastic Processes and their Applications, Elsevier, vol. 124(1), pages 505-521.

    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:130:y:2020:i:10:p:5940-5972. 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/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.