IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v50y2021i17p3972-3987.html
   My bibliography  Save this article

Bayesian bent line quantile regression model

Author

Listed:
  • Yi Li
  • Zongyi Hu

Abstract

This article introduces a Bayesian estimating method for a bent line quantile regression model. Within the Bayesian framework, regression coefficients and threshold can be simultaneously estimated, addressing the problem of optimizing the loss function in frequentist approaches, while the statistical inference on the threshold is direct. Simulation studies and two real data examples show that the Bayesian method demonstrates better sample performance.

Suggested Citation

  • Yi Li & Zongyi Hu, 2021. "Bayesian bent line quantile regression model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(17), pages 3972-3987, August.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:17:p:3972-3987
    DOI: 10.1080/03610926.2019.1710750
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2019.1710750
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2019.1710750?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. Lena Bedawi Elfadli Elmonshid & Omer Ahmed Sayed & Ghadda Mohamed Awad Yousif & Kamal Eldin Hassan Ibrahim Eldaw & Muawya Ahmed Hussein, 2024. "The Impact of Financial Efficiency and Renewable Energy Consumption on CO2 Emission Reduction in GCC Economies: A Panel Data Quantile Regression Approach," Sustainability, MDPI, vol. 16(14), pages 1-16, July.

    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:50:y:2021:i:17:p:3972-3987. 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.