Twostep estimation of functional linear models with applications to longitudinal data

Jianqing Fan JT Zhang

Data Analysis mathscidoc:1912.43260

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62, (2), 303-322, 2000
Functional linear models are useful in longitudinal data analysis. They include many classical and recently proposed statistical models for longitudinal data and other functional data. Recently, smoothing spline and kernel methods have been proposed for estimating their coefficient functions nonparametrically but these methods are either intensive in computation or inefficient in performance. To overcome these drawbacks, in this paper, a simple and powerful twostep alternative is proposed. In particular, the implementation of the proposed approach via local polynomial smoothing is discussed. Methods for estimating standard deviations of estimated coefficient functions are also proposed. Some asymptotic results for the local polynomial estimators are established. Two longitudinal data sets, one of which involves timedependent covariates, are used to demonstrate the approach proposed. Simulation studies
No keywords uploaded!
[ Download ] [ 2019-12-21 11:33:29 uploaded by Jianqing_Fan ] [ 348 downloads ] [ 0 comments ]
@inproceedings{jianqing2000twostep,
  title={Twostep estimation of functional linear models with applications to longitudinal data},
  author={Jianqing Fan, and JT Zhang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113329991247820},
  booktitle={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={62},
  number={2},
  pages={303-322},
  year={2000},
}
Jianqing Fan, and JT Zhang. Twostep estimation of functional linear models with applications to longitudinal data. 2000. Vol. 62. In Journal of the Royal Statistical Society: Series B (Statistical Methodology). pp.303-322. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113329991247820.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved