Adaptive Robust Variable Selection.

@article{Fan2014AdaptiveRV,
  title={Adaptive Robust Variable Selection.},
  author={Jianqing Fan and Yingying Fan and Emre Barut},
  journal={Annals of statistics},
  year={2014},
  volume={42 1},
  pages={324-351}
}
Heavy-tailed high-dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. A natural procedure to address this problem is to use penalized quantile regression with weighted L1-penalty, called weighted robust Lasso (WR-Lasso), in which weights are introduced to ameliorate the bias problem induced by the L1-penalty. In the ultra-high dimensional setting, where the dimensionality can grow exponentially with the sample size, we… CONTINUE READING
Highly Cited
This paper has 26 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 16 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 32 references

Supplement to “ Adaptive robust variable selection

  • J. FAN, Y. FAN, E. BARUT
  • 2014

Quasi - likelihood and / or robust estimation in high dimensions

  • S. VAN DE GEER, P. MÜLLER
  • Statist . Sci .
  • 2012
1 Excerpt

Quasi-likelihood and/or robust estimation

  • Stat. Methodol
  • VAN DE GEER, S. and MÜLLER, P
  • 2012

Similar Papers

Loading similar papers…