IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v47y2018i4p877-889.html
   My bibliography  Save this article

Density estimation via the random forest method

Author

Listed:
  • Kaiyuan Wu
  • Wei Hou
  • Hongbo Yang

Abstract

The problem of density estimation arises naturally in many contexts. In this paper, we consider the approach using a piecewise constant function to approximate the underlying density. We present a new density estimation method via the random forest method based on the Bayesian Sequential Partition (BSP) (Lu, Jiang, and Wong 2013). Extensive simulations are carried out with comparison to the kernel density estimation method, BSP method, and four local kernel density estimation methods. The experiment results show that the new method is capable of providing accurate and reliable density estimation, even at the boundary, especially for i.i.d. data. In addition, the likelihood of the out-of-bag density estimation, which is a byproduct of the training process, is an effective hyperparameter selection criterion.

Suggested Citation

  • Kaiyuan Wu & Wei Hou & Hongbo Yang, 2018. "Density estimation via the random forest method," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(4), pages 877-889, February.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:4:p:877-889
    DOI: 10.1080/03610926.2017.1285929
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2017.1285929
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2017.1285929?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. Laverny, Oskar & Masiello, Esterina & Maume-Deschamps, Véronique & Rullière, Didier, 2021. "Dependence structure estimation using Copula Recursive Trees," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    2. Siong Thye Goh & Lesia Semenova & Cynthia Rudin, 2024. "Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms," INFORMS Joural on Data Science, INFORMS, vol. 3(1), pages 28-48, April.

    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:lstaxx:v:47:y:2018:i:4:p:877-889. 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/lsta .

    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.