Asymptotic normality in the maximum entropy models on graphs with an increasing number of parameters

DOI10.1016/j.jmva.2014.08.013zbMath1304.62038arXiv1308.1768OpenAlexW2088748017MaRDI QIDQ476218

Ting Yan, Yunpeng Zhao, Hong Qin

Publication date: 28 November 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1308.1768

zbMATH Keywords

asymptotic normalitymaximum likelihood estimatorincreasing number of parametersmaximum entropy models

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Asymptotic distribution theory in statistics (62E20)

Related Items (20)

Nonparametric discriminant analysis with network structures in predictor ⋮ Affiliation weighted networks with a differentially private degree sequence ⋮ Network inference from temporally dependent grouped observations ⋮ Asymptotics of score test in the generalized \(\beta\)-model for networks ⋮ Directed weighted random graphs with an increasing bi-degree sequence ⋮ Detection thresholds for the \(\beta\)-model on sparse graphs ⋮ Asymptotics in theβ-model for networks with a differentially private degree sequence ⋮ Asymptotic in the ordered networks with a noisy degree sequence ⋮ Degree-based moment estimation for ordered networks ⋮ A note on asymptotic distributions in a network model with degree heterogeneity and homophily ⋮ Asymptotic in undirected random graph models with a noisy degree sequence ⋮ Inference in a class of directed random graph models with an increasing number of parameters ⋮ A note on asymptotic distributions in a directed network model with degree heterogeneity and homophily ⋮ Asymptotic theory in network models with covariates and a growing number of node parameters ⋮ Bivariate gamma model ⋮ Exponential-family models of random graphs: inference in finite, super and infinite population scenarios ⋮ A note on asymptotic distributions in maximum entropy models for networks ⋮ Asymptotics in directed exponential random graph models with an increasing bi-degree sequence ⋮ Inference using noisy degrees: differentially private \(\beta\)-model and synthetic graphs ⋮ Consistent structure estimation of exponential-family random graph models with block structure

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Consistency under sampling of exponential random graph models
• Maximum likelihood estimation in the \(\beta\)-model
• Random graphs with a given degree sequence
• The method of moments and degree distributions for network models
• Calculation of maximum entropy densities with application to income distribution
• MM algorithms for generalized Bradley-Terry models.
• Consistency of community detection in networks under degree-corrected stochastic block models
• Estimating and understanding exponential random graph models
• A nonparametric view of network models and Newman–Girvan and other modularities
• Instability, Sensitivity, and Degeneracy of Discrete Exponential Families
• Markov Graphs
• On a General Class of Models for Interaction
• An Exponential Family of Probability Distributions for Directed Graphs
• A central limit theorem in the  -model for undirected random graphs with a diverging number of vertices

This page was built for publication: Asymptotic normality in the maximum entropy models on graphs with an increasing number of parameters