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Innovated scalable efficient inference for ultra-large graphical models

Jia Zhou, Zemin Zheng, Huiting Zhou and Ruipeng Dong

Statistics & Probability Letters, 2021, vol. 173, issue C

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.

Keywords: Gaussian graphical models; Scalability; Confidence intervals (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.spl.2021.109085

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