Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model

@article{Chen2016AsymptoticallyNA,
  title={Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model},
  author={Mengjie Chen and Zhao Hui Ren and Hongyu Zhao and Harrison H. Zhou},
  journal={Journal of the American Statistical Association},
  year={2016},
  volume={111},
  pages={394 - 406}
}
  • Mengjie Chen, Zhao Hui Ren, +1 author Harrison H. Zhou
  • Published 2016
  • Mathematics, Medicine
  • Journal of the American Statistical Association
  • We propose an asymptotically normal and efficient procedure to estimate every finite subgraph for covariate-adjusted Gaussian graphical model. As a consequence, a confidence interval as well as p-value can be obtained for each edge. The procedure is tuning-free and enjoys easy implementation and efficient computation through parallel estimation on subgraphs or edges. We apply the asymptotic normality result to perform support recovery through edge-wise adaptive thresholding. This support… CONTINUE READING

    Topics from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 30 CITATIONS

    Inference for High-dimensional Exponential Family Graphical Models

    VIEW 2 EXCERPTS
    CITES BACKGROUND & METHODS

    FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks

    VIEW 2 EXCERPTS
    CITES METHODS

    Joint Estimation of Multiple Conditional Gaussian Graphical Models

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs

    VIEW 1 EXCERPT
    CITES BACKGROUND

    Learning Dynamic Conditional Gaussian Graphical Models

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    False discovery rate control for high dimensional networks of quantile associations conditioning on covariates.

    • Jichun Xie, Ruosha Li
    • Mathematics, Medicine
    • Journal of the Royal Statistical Society. Series B, Statistical methodology
    • 2018
    VIEW 5 EXCERPTS
    CITES BACKGROUND & METHODS
    HIGHLY INFLUENCED

    FILTER CITATIONS BY YEAR

    2014
    2020

    CITATION STATISTICS

    • 1 Highly Influenced Citations

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 57 REFERENCES

    Covariate Adjusted Precision Matrix Estimation with an Application in Genetical Genomics

    • C OVARIATE A DJUSTED P RECISION M ATRIX E STIMATION
    • 2011
    VIEW 25 EXCERPTS
    HIGHLY INFLUENTIAL

    Sparse Estimation of Conditional Graphical Models With Application to Gene Networks

    VIEW 1 EXCERPT

    Partial Correlation Estimation by Joint Sparse Regression Models

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Pivotal Estimation in High-Dimensional Regression via Linear Programming

    VIEW 1 EXCERPT