SEMIPARAMETRIC GEE ANALYSIS IN PARTIALLY LINEAR SINGLE-INDEX MODELS FOR LONGITUDINAL DATA
KAUST Grant NumberKUS-CI-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/673033
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AbstractIn this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on a combination of the local linear smoothing and generalized estimation equations (GEE) is introduced to estimate the two parameter vectors as well as the unknown link function. Under some mild conditions, we derive the asymptotic properties of the proposed parametric and nonparametric estimators in different scenarios, from which we find that the convergence rates and asymptotic variances of the proposed estimators for sparse longitudinal data would be substantially different from those for dense longitudinal data. We also discuss the estimation of the covariance (or weight) matrices involved in the semiparametric GEE method. Furthermore, we provide some numerical studies including Monte Carlo simulation and an empirical application to illustrate our methodology and theory.
CitationChen, J., Li, D., Liang, H., & Wang, S. (2015). Semiparametric GEE analysis in partially linear single-index models for longitudinal data. The Annals of Statistics, 43(4). doi:10.1214/15-aos1320
SponsorsSupported in part by NSF Grants DMS-14-40121 and DMS-14-18042 and by Award Number 11228103, made by National Natural Science Foundation of China.; Supported in part by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).
PublisherINST MATHEMATICAL STATISTICS
JournalANNALS OF STATISTICS