Generalized empirical likelihood methods for analyzing longitudinal data
AbstractEfficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks theorem for the limiting distributions of the empirical likelihood ratios is derived. It is shown that one of the proposed methods is locally efficient among a class of within-subject variance-covariance matrices. A simulation study is conducted to investigate the finite sample properties of the proposed methods and compare them with the block empirical likelihood method by You et al. (2006) and the normal approximation with a correctly estimated variance-covariance. The results suggest that the proposed methods are generally more efficient than existing methods which ignore the correlation structure, and better in coverage compared to the normal approximation with correctly specified within-subject correlation. An application illustrating our methods and supporting the simulation study results is also presented.
CitationWang S, Qian L, Carroll RJ (2010) Generalized empirical likelihood methods for analyzing longitudinal data. Biometrika 97: 79–93. Available: http://dx.doi.org/10.1093/biomet/asp073.
SponsorsWe thank the editor, the associate editor and two referees for their helpful comments and suggestionsthat have led to significant improvements of this paper. Carroll’s research was supportedby a grant from the National Cancer Institute and by a research award made by the King AbdullahUniversity of Science and Technology.
PublisherOxford University Press (OUP)
PubMed Central IDPMC2841365
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