IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v36y2020i1p77-94.html
   My bibliography  Save this article

Nonparametric universal copula modeling

Author

Listed:
  • Subhadeep Mukhopadhyay
  • Emanuel Parzen

Abstract

To handle the ubiquitous problem of “dependence learning,” copulas are quickly becoming a pervasive tool across a wide range of data‐driven disciplines encompassing neuroscience, finance, econometrics, genomics, social science, machine learning, healthcare, and many more. At the same time, despite their practical value, the empirical methods of “learning copula from data” have been unsystematic with full of case‐specific recipes. Taking inspiration from modern LP‐nonparametrics, this paper presents a modest contribution to the need for a more unified and structured approach of copula modeling that is simultaneously valid for arbitrary combinations of continuous and discrete variables.

Suggested Citation

  • Subhadeep Mukhopadhyay & Emanuel Parzen, 2020. "Nonparametric universal copula modeling," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(1), pages 77-94, January.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:1:p:77-94
    DOI: 10.1002/asmb.2503
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.2503
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.2503?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
    ---><---

    Citations

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


    Cited by:

    1. Deep Mukhopadhyay, 2021. "Abductive Inference and C. S. Peirce: 150 Years Later," Papers 2111.08054, arXiv.org, revised Feb 2023.
    2. Subhadeep Mukhopadhyay, 2023. "Modelplasticity and abductive decision making," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 255-276, June.
    3. Mukhopadhyay, Subhadeep & Wang, Kaijun, 2023. "On The Problem of Relevance in Statistical Inference," Econometrics and Statistics, Elsevier, vol. 25(C), pages 93-109.
    4. Subhadeep & Mukhopadhyay, 2021. "A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications," Papers 2108.09438, arXiv.org, revised Aug 2022.
    5. Subhadeep & Mukhopadhyay, 2022. "Modelplasticity and Abductive Decision Making," Papers 2203.03040, arXiv.org, revised Mar 2023.
    6. Subhadeep Mukhopadhyay, 2023. "Abductive Inference and C. S. Peirce: 150 Years Later," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 123-149, March.

    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:wly:apsmbi:v:36:y:2020:i:1:p:77-94. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.