Consistency and asymptotic normality of stochastic block models estimators from sampled data

DOI10.1214/20-EJS1750zbMath1454.62286arXiv1903.12488OpenAlexW3092298211MaRDI QIDQ2209826

Mahendra Mariadassou, Timothée Tabouy

Publication date: 5 November 2020

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1903.12488

zbMATH Keywords

asymptotic normalitymaximum likelihoodmissing dataconcentration inequalitystochastic block model

Mathematics Subject Classification ID

Neural nets and related approaches to inference from stochastic processes (62M45) Missing data (62D10)

Related Items (2)

Maximum likelihood estimation of sparse networks with missing observations ⋮ Estimation of dense stochastic block models visited by random walks

Cites Work

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• Likelihood-based model selection for stochastic block models
• Variational Inference for Stochastic Block Models From Sampled Data
• Modeling heterogeneity in random graphs through latent space models: a selective review
• Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels
• Spectral clustering and the high-dimensional stochastic blockmodel
• A central limit theorem for random sums of random variables
• Modeling social networks from sampled data
• Uncovering latent structure in valued graphs: a variational approach
• Statistical analysis of network data. Methods and models
• Estimation and prediction for stochastic blockmodels for graphs with latent block structure
• Consistency of maximum-likelihood and variational estimators in the stochastic block model
• Consistency and asymptotic normality of latent block model estimators
• Matrix estimation by universal singular value thresholding
• Convergence of the groups posterior distribution in latent or stochastic block models
• Stochastic blockmodels with a growing number of classes
• Inference and missing data
• Estimation and Prediction for Stochastic Blockstructures
• New Consistent and Asymptotically Normal Parameter Estimates for Random-Graph Mixture Models
• Learning latent block structure in weighted networks
• Cluster Inference by Using Transitivity Indices in Empirical Graphs
• Variational Bayesian inference and complexity control for stochastic block models
• A Class of Statistics with Asymptotically Normal Distribution

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