2019/03/28 by Mahendra Mariadassou, Mariadassou, Mahendra, Timothée Tabouy +1 · 1 citation
Mathematics · #FOS: Mathematics #Markov Chains and Monte Carlo Methods #Random Matrices and Applications #Statistical Methods and Inference #Statistics Theory (math.ST) #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1903.12488
32 pages, 0 figures. arXiv admin note: text overlap with arXiv:1704.06629
openalex publication_date 2019/03/28 · arxiv created 2020/10/20 · arxiv updated 2020/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Statistical analysis of network is an active research area and the literature counts a lot of papers concerned with network models and statistical analysis of networks. However, very few papers deal with missing data in network analysis and we reckon that, in practice, networks are often observed with missing values. In this paper we focus on the Stochastic Block Model with valued edges and consider a MCAR setting by assuming that every dyad (pair of nodes) is sampled identically and independently of the others with probability ρ> 0. We prove that maximum likelihood estimators and its variational approximations are consistent and asymptotically normal in the presence of missing data as soon as the sampling probability ρ satisfies ρ≫log(n)/n.