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• W3170356687 abstract "Network regression models, where the outcome comprises the valued edge in a network and the predictors are actor or dyad-level covariates, are used extensively in the social and biological sciences. Valid inference relies on accurately modeling the residual dependencies among the relations. Frequently homogeneity assumptions are placed on the errors which are commonly incorrect and ignore critical, natural clustering of the actors. In this work, we present a novel regression modeling framework that models the errors as resulting from a community-based dependence structure and exploits the subsequent exchangeability properties of the error distribution to obtain parsimonious standard errors for regression parameters." @default.
• W3170356687 created "2021-06-22" @default.
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• W3170356687 date "2021-06-08" @default.
• W3170356687 modified "2023-10-01" @default.
• W3170356687 title "Inference for Network Regression Models with Community Structure" @default.
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• W3170356687 hasPublicationYear "2021" @default.
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