Abstract
In the analysis of longitudinal or panel data, neglecting the serial correlations among the repeated measurements within subjects may lead to inefficient inference. In particular, when the number of repeated measurements is large, it may be desirable to model the serial correlations more generally. An appealing approach is to accommodate the serial correlations nonparametrically. In this article, we propose a moving blocks empirical likelihood method for general estimating equations. Asymptotic results are derived under sequential limits. Simulation studies are conducted to investigate the finite sample performances of the proposed methods and compare them with the elementwise and subject-wise empirical likelihood methods of Wang et al. (2010, Biometrika 97, 79-93) and the block empirical likelihood method of You et al. (2006, Can. J. Statist. 34, 79-96). An application to an AIDS longitudinal study is presented.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
