New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis

Jianqing Fan Runze Li

Statistics Theory and Methods mathscidoc:1912.43264

Journal of the American Statistical Association, 99, (467), 710-723, 2004.9
Semiparametric regression models are very useful for longitudinal data analysis. The complexity of semiparametric models and the structure of longitudinal data pose new challenges to parametric inferences and model selection that frequently arise from longitudinal data analysis. In this article, two new approaches are proposed for estimating the regression coefficients in a semiparametric model. The asymptotic normality of the resulting estimators is established. An innovative class of variable selection procedures is proposed to select significant variables in the semiparametric models. The proposed procedures are distinguished from others in that they simultaneously select significant variables and estimate unknown parameters. Rates of convergence of the resulting estimators are established. With a proper choice of regularization parameters and penalty functions, the proposed variable selection procedures
No keywords uploaded!
[ Download ] [ 2019-12-21 11:33:47 uploaded by Jianqing_Fan ] [ 206 downloads ] [ 0 comments ]
@inproceedings{jianqing2004new,
  title={New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis},
  author={Jianqing Fan, and Runze Li},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113347385716824},
  booktitle={Journal of the American Statistical Association},
  volume={99},
  number={467},
  pages={710-723},
  year={2004},
}
Jianqing Fan, and Runze Li. New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. 2004. Vol. 99. In Journal of the American Statistical Association. pp.710-723. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113347385716824.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved