Skip Navigation

Biometrika 1995 82(3):461-477; doi:10.1093/biomet/82.3.461
© 1995 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by HANFELT, J. J.
Right arrow Articles by LIANG, K.-Y.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Articles

Approximate likelihood ratios for general estimating functions

JOHN J. HANFELT1 and KUNG-YEE LIANG2

1 Biostatistics Unit, Georgetown University Medical Center, Washington DC 20007, U. S.A.
2 Department of Biostatistics, Johns Hopkins University, Baltimore Maryland 21205, U. S.A.

Received for publication 1 March 1994. Revision received 1 February 1995.
  Abstract

The method of estimating functions (Godambe, 1991) is commonly used when one desires to conduct inference about some parameters of interest but the full distribution of the observations is unknown. However, this approach may have limited utility, due to multiple roots for the estimating function, a poorly behaved Wald test, or lack of a goodness-of-fit test. This paper presents approximate likelihood ratios that can be used along with estimating functions when any of these three problems occurs. We show that the approximate likelihood ratio provides correct large sample inference under very general circumstances, including clustered data and misspecified weights in the estimating function. Two methods of constructing the approximate likelihood ratio, one based on the quasi-likelihood approach and the other based on the linear projection approach, are compared and shown to be closely related. In particular we show that quasi-likelihood is the limit of the projection approach. We illustrate the technique with two applications.

Key Words: Approximate likelihood • Estimating function • Multiple roots • Projection • Quasi-likelihood


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.