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Robust high dimensional factor models with applications to statistical machine learning

Jianqing Fan Kaizheng Wang Yiqiao Zhong Ziwei Zhu

Machine Learning mathscidoc:1912.43399

arXiv preprint arXiv:1808.03889, 2018.8
Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges.
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@inproceedings{jianqing2018robust,
  title={Robust high dimensional factor models with applications to statistical machine learning},
  author={Jianqing Fan, Kaizheng Wang, Yiqiao Zhong, and Ziwei Zhu},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114203369865959},
  booktitle={arXiv preprint arXiv:1808.03889},
  year={2018},
}
Jianqing Fan, Kaizheng Wang, Yiqiao Zhong, and Ziwei Zhu. Robust high dimensional factor models with applications to statistical machine learning. 2018. In arXiv preprint arXiv:1808.03889. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114203369865959.
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