Semiparametric estimation of covariance matrixes for longitudinal data

Jianqing Fan Yichao Wu

Statistics Theory and Methods mathscidoc:1912.43311

Journal of the American Statistical Association, 103, (484), 1520-1533, 2008.12
Estimation of longitudinal data covariance structure poses significant challenges because the data usually are collected at irregular time points. A viable semiparametric model for covariance matrixes has been proposed that allows one to estimate the variance function nonparametrically and to estimate the correlation function parametrically by aggregating information from irregular and sparse data points within each subject. But the asymptotic properties of the quasi-maximum likelihood estimator (QMLE) of parameters in the covariance model are largely unknown. We address this problem in the context of more general models for the conditional mean function, including parametric, nonparametric, or semiparametric. We also consider the possibility of rough mean regression function and introduce the difference-based method to reduce biases in the context of varying-coefficient partially linear mean regression
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@inproceedings{jianqing2008semiparametric,
  title={Semiparametric estimation of covariance matrixes for longitudinal data},
  author={Jianqing Fan, and Yichao Wu},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113633587732871},
  booktitle={Journal of the American Statistical Association},
  volume={103},
  number={484},
  pages={1520-1533},
  year={2008},
}
Jianqing Fan, and Yichao Wu. Semiparametric estimation of covariance matrixes for longitudinal data. 2008. Vol. 103. In Journal of the American Statistical Association. pp.1520-1533. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113633587732871.
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