IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v68y2004i3p305-314.html
   My bibliography  Save this article

A new method of calibration for the empirical loglikelihood ratio

Author

Listed:
  • Tsao, Min

Abstract

The Chi-square calibration for the empirical loglikelihood ratio refers to the method of approximating quantiles of the finite sample distribution of the empirical loglikelihood ratio with that of the limiting Chi-square distribution. Empirical likelihood ratio confidence regions are usually computed with the Chi-square calibration. Such Chi-square calibrated confidence regions can have a serious undercoverage problem. This paper examines the undercoverage problem from a finite sample standpoint and proposes a method of calibration which approximates the finite sample distributions with a new family of distributions. The new distributions is another family of sampling distributions arising from the normal distributions and is derived through a simple finite sample similarity between the empirical and parametric likelihoods. The new method of calibration is as easy to use as the Chi-square calibration. It corrects the undercoverage problem of the Chi-square calibration and is consistently more accurate.

Suggested Citation

  • Tsao, Min, 2004. "A new method of calibration for the empirical loglikelihood ratio," Statistics & Probability Letters, Elsevier, vol. 68(3), pages 305-314, July.
    • Handle: RePEc:eee:stapro:v:68:y:2004:i:3:p:305-314
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(04)00121-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Tsao, Min, 2001. "A small sample calibration method for the empirical likelihood ratio," Statistics & Probability Letters, Elsevier, vol. 54(1), pages 41-45, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Yongcheng Qi, 2010. "On the tail index of a heavy tailed distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(2), pages 277-298, April.
    2. Li, Yizeng & Qi, Yongcheng, 2019. "Adjusted empirical likelihood method for the tail index of a heavy-tailed distribution," Statistics & Probability Letters, Elsevier, vol. 152(C), pages 50-58.

    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.

      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:stapro:v:68:y:2004:i:3:p:305-314. 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/622892/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.