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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/265

Title: Bootstrap tests for misspecified models, with application to clustered binary data
Authors: AERTS, Marc
Keywords: Computer intensive
Clustered data
Non and semiparametric methods
Issue Date: 2001
Citation: Computational Statistics and Data Analysis, 36(3). p. 383-401
Abstract: When the data do not come from the assumed parametric model, the usual asymptotic chi-squared distribution under the null hypothesis, remains valid for "robustified" Wald and score test statistics. In this paper, we compare the performance of this chi-squared approximation to that of a semiparametric bootstrap method. The bootstrap approximation is based on a one-step bootstrap estimator reflecting the null hypothesis. One of the advantages of this one-step approach is that no bootstrap data have to be generated and no additional model fitting is required. Simulations on clustered binary data indicate that the robust score test is superior and that, in cases where the chi-squared type tests fail in reaching the prescribed significance level, the proposed bootstrap test succeeds in correcting this towards the nominal level. The different methods are also compared on real developmental toxicity data.
URI: http://hdl.handle.net/1942/265
DOI: 10.1016/S0167-9473(00)00051-7
ISI #: 000168792200007
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2002
Appears in Collections: Research publications

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