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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/332

Title: Pseudo-likelihood inference for clustered binary data
Authors: Geys, Helena
Molenberghs, Geert
Ryan, Louise M.
Keywords: Clustered data
Mathematical Statistics
Categorical data
Issue Date: 1997
Citation: Communications in Statistics: Theory and Methods, 26(11). p. 2743-2767
Abstract: Molenberghs and Ryan (1996) proposed a likelihood-based model for clustered binary data, based on a multivariate exponential family model (Cox, 1972). The model benefits from the elegance and simplicity of exponential family theory and is flexible in terms of allowing response rates to depend on cluster size. A main problem however, particularly with large clusters is the evaluation of the normalizing constant. In this paper, pseudo-likelihood is explored as an alternative mode of inference. The pseudo-likelihood equations are derived, the model is applied to data from a developmental toxicity study, and an asymptotic and small sample relative efficiency study is performed.
URI: http://hdl.handle.net/1942/332
DOI: 10.1080/03610929708832075
ISI #: A1997YF14300011
ISSN: 0361-0926
Type: Journal Contribution
Appears in Collections: Research publications

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