Innovated scalable efficient inference for ultra-large graphical models
Huiting Zhou and
Statistics & Probability Letters, 2021, vol. 173, issue C
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)
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