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Non?parametric regression for networks

Severn, Katie E; Dryden, Ian L; Preston, Simon P

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Authors

IAN DRYDEN IAN.DRYDEN@NOTTINGHAM.AC.UK
Professor of Statistics

SIMON PRESTON simon.preston@nottingham.ac.uk
Professor of Statistics and Applied Mathematics



Abstract

Network data are becoming increasingly available, and so there is a need to develop suitable methodology for statistical analysis. Networks can be represented as graph Laplacian matrices, which are a type of manifold-valued data. Our main objective is to estimate a regression curve from a sample of graph Laplacian matrices conditional on a set of Euclidean covariates, for example in dynamic networks where the covariate is time. We develop an adapted Nadaraya-Watson estimator which has uniform weak consistency for estimation using Euclidean and power Euclidean metrics. We apply the methodology to the Enron email corpus to model smooth trends in monthly networks and highlight anomalous networks. Another motivating application is given in corpus linguistics, which explores trends in an author's writing style over time based on word co-occurrence networks.

Citation

Severn, K. E., Dryden, I. L., & Preston, S. P. (2021). Non?parametric regression for networks. Stat, 10(1), Article e373. https://doi.org/10.1002/sta4.373

Journal Article Type Article
Acceptance Date Jan 20, 2021
Online Publication Date Mar 1, 2021
Publication Date 2021-12
Deposit Date Feb 4, 2021
Publicly Available Date Mar 29, 2024
Journal Stat
Electronic ISSN 2049-1573
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 10
Issue 1
Article Number e373
DOI https://doi.org/10.1002/sta4.373
Public URL https://nottingham-repository.worktribe.com/output/5292341
Publisher URL https://onlinelibrary.wiley.com/doi/abs/10.1002/sta4.373

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