1 citations · 1 across the 4 of their papers we have counts for
4 papers
Generalized network autoregressive modelling of longitudinal networks with application to presidential elections in the USA
Guy Nason, Daniel Salnikov, Mario Cortina-Borja
Longitudinal networks are becoming increasingly relevant in the study of dynamic processes characterised by known or inferred community structure. Generalised Network Autoregressiv…
Modelling clusters in network time series with an application to presidential elections in the USA
Guy Nason, Daniel Salnikov, Mario Cortina-Borja
Network time series are becoming increasingly relevant in the study of dynamic processes characterised by a known or inferred underlying network structure. Generalised Network Auto…
New Methods for Network Count Time Series
Hengxu Liu, Guy Nason
The original generalized network autoregressive models are poor for modelling count data as they are based on the additive and constant noise assumptions, which is usually inapprop…
New tools for network time series with an application to COVID-19 hospitalisations
Guy Nason, Daniel Salnikov, Mario Cortina-Borja
Network time series are becoming increasingly important across many areas in science and medicine and are often characterised by a known or inferred underlying network structure, w…