Variance estimation using refitted crossvalidation in ultrahigh dimensional regression

Jianqing Fan Shaojun Guo Ning Hao

Statistics Theory and Methods mathscidoc:1912.43285

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74, (1), 37-65, 2012.1
Variance estimation is a fundamental problem in statistical modelling. In ultrahigh dimensional linear regression where the dimensionality is much larger than the sample size, traditional variance estimation techniques are not applicable. Recent advances in variable selection in ultrahigh dimensional linear regression make this problem accessible. One of the major problems in ultrahigh dimensional regression is the high spurious correlation between the unobserved realized noise and some of the predictors. As a result, the realized noises are actually predicted when extra irrelevant variables are selected, leading to a serious underestimate of the level of noise. We propose a twostage refitted procedure via a data splitting technique, called refitted crossvalidation, to attenuate the influence of irrelevant variables with high spurious correlations. Our asymptotic results show that the resulting procedure performs as
No keywords uploaded!
[ Download ] [ 2019-12-21 11:35:00 uploaded by Jianqing_Fan ] [ 295 downloads ] [ 0 comments ]
@inproceedings{jianqing2012variance,
  title={Variance estimation using refitted crossvalidation in ultrahigh dimensional regression},
  author={Jianqing Fan, Shaojun Guo, and Ning Hao},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113500212827845},
  booktitle={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={74},
  number={1},
  pages={37-65},
  year={2012},
}
Jianqing Fan, Shaojun Guo, and Ning Hao. Variance estimation using refitted crossvalidation in ultrahigh dimensional regression. 2012. Vol. 74. In Journal of the Royal Statistical Society: Series B (Statistical Methodology). pp.37-65. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113500212827845.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved