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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/261

Title: Bootstrapping local polynomial estimators in likelihood-based models
Authors: CLAESKENS, Gerda
Keywords: Non and semiparametric methods
Computer intensive
Clustered data
Issue Date: 2000
Citation: Journal of Statistical Planning and Inference, 86(1). p. 63-80
Abstract: The local likelihood estimator and a semiparametric bootstrap method are studied under weaker conditions than usual; it is not assumed that the true probability distribution underlying the observations is known and hence the local likelihood estimator might be based on an incorrect likelihood. Moreover, results are generalized to pseudolikelihood, which is based on a product of conditional densities. Strong consistency and asymptotic normality are derived under suitable regularity conditions and a study of the derivatives of the estimators is performed. It is shown that the bootstrap method leads to consistent estimators which can be used for constructing confidence regions. As an illustration, the local likelihood smoother and the bootstrap procedure are implemented for a selection of probability models for clustered binary data. A data example shows the method's applicability.
URI: http://hdl.handle.net/1942/261
DOI: 10.1016/S0378-3758(99)00154-8
ISI #: 000085999800005
ISSN: 0378-3758
Category: A1
Type: Journal Contribution
Validation: ecoom, 2001
Appears in Collections: Research publications

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