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Innovated scalable efficient inference for ultra-large graphical models

Author

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  • Zhou, Jia
  • Zheng, Zemin
  • Zhou, Huiting
  • Dong, Ruipeng

Abstract

Statistical inference for ultra-large graphical models is important in network data analysis. We exploit the innovated scalable efficient estimation (Fan and Lv, 2016) as an initial estimate to develop a scalable inference procedure for graphical models. The effectiveness of the proposed method is theoretically and numerically demonstrated.

Suggested Citation

  • Zhou, Jia & Zheng, Zemin & Zhou, Huiting & Dong, Ruipeng, 2021. "Innovated scalable efficient inference for ultra-large graphical models," Statistics & Probability Letters, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:stapro:v:173:y:2021:i:c:s016771522100047x
    DOI: 10.1016/j.spl.2021.109085
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    References listed on IDEAS

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