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 AERTS, Marc |
Issue Date: | 2000 | Publisher: | ELSEVIER SCIENCE BV | Source: | 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. | Document URI: | http://hdl.handle.net/1942/261 | DOI: | 10.1016/S0378-3758(99)00154-8 | ISI #: | 000085999800005 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2001 |
Appears in Collections: | Research publications |
Show full item record
SCOPUSTM
Citations
9
checked on Sep 7, 2020
WEB OF SCIENCETM
Citations
10
checked on Sep 26, 2024
Page view(s)
94
checked on Jun 7, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.