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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/257

Title: Bootstrapping pseudolikelihood models for clustered binary data
Authors: AERTS, Marc
Keywords: Clustered data
Computer intensive
Issue Date: 1999
Publisher: KLUWER
Citation: Annals of the Institute of Statistical Mathematics, 51(3). p. 515-530
Abstract: Asymptotic properties of the parametric bootstrap procedure for maximum pseudolikelihood estimators and hypothesis tests are studied in the general framework of associated populations. The technique is applied to the analysis of toxicological experiments which, based on pseudolikelihood inference for clustered binary data, fits into this framework. It is shown that the bootstrap approximation can be used as an interesting alternative to the classical asymptotic distribution of estimators and test statistics. Finite sample simulations for clustered binary data models confirm the asymptotic theory and indicate some substantial improvements.
URI: http://hdl.handle.net/1942/257
DOI: 10.1023/A:1003902206203
ISI #: 000083438400007
ISSN: 0020-3157
Type: Journal Contribution
Validation: ecoom, 2000
Appears in Collections: Research publications

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