Composite Likelihood Inference by Nonparametric Saddlepoint Tests
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.
|Date of creation:||Jun 2013|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: 040 676 7048
Fax: 040 54637
Web page: http://www.deams.units.it/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:tre:wpaper:12. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gianni Perini)
If references are entirely missing, you can add them using this form.