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A projection based conditional dependence measure with applications to high-dimensional undirected graphical models

Jianqing Fan Yang Feng Lucy Xia

Statistics Theory and Methods mathscidoc:1912.43397

arXiv preprint arXiv:1501.01617, 2020.1
Measuring conditional dependence is an important topic in statistics with broad applications including graphical models. Under a factor model setting, a new conditional dependence measure based on projection is proposed. The corresponding conditional independence test is developed with the asymptotic null distribution unveiled where the number of factors could be high-dimensional. It is also shown that the new test has control over the asymptotic significance level and can be calculated efficiently. A generic method for building dependency graphs without Gaussian assumption using the new test is elaborated. Numerical results and real data analysis show the superiority of the new method.
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@inproceedings{jianqing2020a,
  title={A projection based conditional dependence measure with applications to high-dimensional undirected graphical models},
  author={Jianqing Fan, Yang Feng, and Lucy Xia},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114152147312957},
  booktitle={arXiv preprint arXiv:1501.01617},
  year={2020},
}
Jianqing Fan, Yang Feng, and Lucy Xia. A projection based conditional dependence measure with applications to high-dimensional undirected graphical models. 2020. In arXiv preprint arXiv:1501.01617. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221114152147312957.
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