IDEAS home Printed from https://ideas.repec.org/p/tre/wpaper/12.html
   My bibliography  Save this paper

Composite Likelihood Inference by Nonparametric Saddlepoint Tests

Author

Listed:
  • Lunardon, Nicola
  • Ronchetti, Elvezio

Abstract

The class of composite likelihood functions provides a flexible and powerful toolkit to carry out approximate inference for complex statistical models when the full likelihood is either impossible to specify or unfeasible to compute. However, the strength of the composite likelihood approach is dimmed when considering hypothesis testing about a multidimensional parameter because the finite sample behavior of likelihood ratio, Wald, and score-type test statistics is tied to the Godambe information matrix. Consequently inaccurate estimates of the Godambe information translate in inaccurate p-values. In this paper it is shown how accurate inference can be obtained by using a fully nonparametric saddlepoint test statistic derived from the composite score functions. The proposed statistic is asymptotically chi-square distributed up to a relative error of second order and does not depend on the Godambe information. The validity of the method is demonstrated through simulation studies.

Suggested Citation

  • Lunardon, Nicola & Ronchetti, Elvezio, 2013. "Composite Likelihood Inference by Nonparametric Saddlepoint Tests," Working Papers DEAMS 12, DEAMS - Dipartimento di Scienze Economiche, Aziendali, Matematiche e Statistiche "Bruno de Finetti".
    • Handle: RePEc:tre:wpaper:12
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10077/8806
    Download Restriction: no
    ---><---

    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:tre:wpaper:12. 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: Gianni Perini (email available below). General contact details of provider: https://edirc.repec.org/data/detriit.html .

    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.