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Analysis of longitudinal data with semiparametric estimation of covariance function

Jianqing Fan Tao Huang Runze Li

Statistics Theory and Methods mathscidoc:1912.43277

Journal of the American Statistical Association, 102, (478), 632-641, 2007.6
Improving efficiency for regression coefficients and predicting trajectories of individuals are two important aspects in the analysis of longitudinal data. Both involve estimation of the covariance function. Yet challenges arise in estimating the covariance function of longitudinal data collected at irregular time points. A class of semiparametric models for the covariance function by that imposes a parametric correlation structure while allowing a nonparametric variance function is proposed. A kernel estimator for estimating the nonparametric variance function is developed. Two methods for estimating parameters in the correlation structurea quasi-likelihood approach and a minimum generalized variance methodare proposed. A semiparametric varying coefficient partially linear model for longitudinal data is introduced, and an estimation procedure for model coefficients using a profile weighted least squares approach
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@inproceedings{jianqing2007analysis,
  title={Analysis of longitudinal data with semiparametric estimation of covariance function},
  author={Jianqing Fan, Tao Huang, and Runze Li},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113433764740837},
  booktitle={Journal of the American Statistical Association},
  volume={102},
  number={478},
  pages={632-641},
  year={2007},
}
Jianqing Fan, Tao Huang, and Runze Li. Analysis of longitudinal data with semiparametric estimation of covariance function. 2007. Vol. 102. In Journal of the American Statistical Association. pp.632-641. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113433764740837.
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