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https://doi.org/10.1093/biomet/91.2.447

has been cited by the following article:

• TITLE: Empirical Likelihood Inference for Generalized Partially Linear Models with Longitudinal Data

  AUTHORS: Jinghua Zhang, Liugen Xue

  KEYWORDS: Longitudinal Data, Generalized Partially Linear Models, Empirical Likelihood, Quadratic Inference Function

  JOURNAL NAME: Open Journal of Statistics, Vol.10 No.2, April 3, 2020

  ABSTRACT: In this article, we propose a generalized empirical likelihood inference for the parametric component in semiparametric generalized partially linear models with longitudinal data. Based on the extended score vector, a generalized empirical likelihood ratios function is defined, which integrates the within-clustercorrelation meanwhile avoids direct estimating the nuisance parameters in the correlation matrix. We show that the proposed statistics are asymptoticallyChi-squared under some suitable conditions, and hence it can be used to construct the confidence region of parameters. In addition, the maximum empirical likelihood estimates of parameters and the corresponding asymptotic normality are obtained. Simulation studies demonstrate the performance of the proposed method.