IDEAS home Printed from https://ideas.repec.org/a/taf/sactxx/v2018y2018i6p545-554.html
   My bibliography  Save this article

Dirichlet process mixture models for insurance loss data

Author

Listed:
  • Liang Hong
  • Ryan Martin

Abstract

In the recent insurance literature, a variety of finite-dimensional parametric models have been proposed for analyzing the hump-shaped, heavy-tailed, and highly skewed loss data often encountered in applications. These parametric models are relatively simple, but they lack flexibility in the sense that an actuary analyzing a new data-set cannot be sure that any one of these parametric models will be appropriate. As a consequence, the actuary must make a non-trivial choice among a collection of candidate models, putting him/herself at risk for various model misspecification biases. In this paper, we argue that, at least in cases where prediction of future insurance losses is the ultimate goal, there is reason to consider a single but more flexible nonparametric model. We focus here on Dirichlet process mixture models, and we reanalyze several of the standard insurance data-sets to support our claim that model misspecification biases can be avoided by taking a nonparametric approach, with little to no cost, compared to existing parametric approaches.

Suggested Citation

  • Liang Hong & Ryan Martin, 2018. "Dirichlet process mixture models for insurance loss data," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2018(6), pages 545-554, July.
  • Handle: RePEc:taf:sactxx:v:2018:y:2018:i:6:p:545-554
    DOI: 10.1080/03461238.2017.1402086
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03461238.2017.1402086
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/03461238.2017.1402086?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.

    Citations

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


    Cited by:

    1. Tianxing Yan & Yi Lu & Himchan Jeong, 2024. "Dependence Modelling for Heavy-Tailed Multi-Peril Insurance Losses," Risks, MDPI, vol. 12(6), pages 1-17, June.

    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:taf:sactxx:v:2018:y:2018:i:6:p:545-554. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/sact .

    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.