Averages of unlabeled networks: geometric characterization and asymptotic behavior

DOI10.1214/19-AOS1820zbMath1439.62068arXiv1709.02793OpenAlexW3005928918MaRDI QIDQ2176631

Yanyan Li

Publication date: 5 May 2020

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1709.02793

zbMATH Keywords

law of large numberscentral limit theoremfundamental domainundirected graphFréchet mean

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Statistics on manifolds (62R30) Central limit and other weak theorems (60F05) Applications of graph theory (05C90) Global Riemannian geometry, including pinching (53C20) Probabilistic graphical models (62H22) Applications of differential geometry to data and computer science (53Z50)

Related Items (14)

Modeling Network Populations via Graph Distances ⋮ Manifold valued data analysis of samples of networks, with applications in corpus linguistics ⋮ Averages of unlabeled networks: geometric characterization and asymptotic behavior ⋮ Matrix means and a novel high-dimensional shrinkage phenomenon ⋮ Hypothesis testing for populations of networks ⋮ Limit theorems for Fréchet mean sets ⋮ A Spectral-Based Framework for Hypothesis Testing in Populations of Networks ⋮ Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome ⋮ Intrinsic and extrinsic deep learning on manifolds ⋮ Central limit theorems on compact metric spaces ⋮ Tukey’s Depth for Object Data ⋮ A quotient space formulation for generative statistical analysis of graphical data ⋮ Extrinsic Gaussian processes for regression and classification on manifolds ⋮ The Fréchet mean of inhomogeneous random graphs

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