{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["17(11)"],"submitter":["Chan K"],"pubmed_abstract":["In this paper, we propose a portmanteau test for whether a graph-structured network dataset without replicates exhibits autocorrelation across units connected by edges. Specifically, the well known Ljung-Box test for serial autocorrelation of time series data is generalized to the network setting using a specially derived central limit theorem for a weakly stationary random field. The asymptotic distribution of the test statistic under the null hypothesis of no autocorrelation is shown to be chi-squared, yielding a simple and easy-to-implement procedure for testing graph-structured autocorrelation, including spatial and spatial-temporal autocorrelation as special cases. Numerical simulations are carried out to demonstrate and confirm the derived asymptotic results. Convergence is found to "],"journal":["PloS one"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9632870"],"repository":["biostudies-literature"],"pubmed_title":["Testing network autocorrelation without replicates"],"pmcid":["PMC9632870"],"pubmed_authors":["Kennedy A","Chan K","Han J","Yam S"],"additional_accession":[]},"is_claimable":false,"name":"Testing network autocorrelation without replicates","description":"In this paper, we propose a portmanteau test for whether a graph-structured network dataset without replicates exhibits autocorrelation across units connected by edges. Specifically, the well known Ljung-Box test for serial autocorrelation of time series data is generalized to the network setting using a specially derived central limit theorem for a weakly stationary random field. The asymptotic distribution of the test statistic under the null hypothesis of no autocorrelation is shown to be chi-squared, yielding a simple and easy-to-implement procedure for testing graph-structured autocorrelation, including spatial and spatial-temporal autocorrelation as special cases. Numerical simulations are carried out to demonstrate and confirm the derived asymptotic results. Convergence is found to ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2025-04-04T07:45:30.925Z","creation":"2025-02-19T01:40:01.951Z"},"accession":"S-EPMC9632870","cross_references":{}}