activity
20162020
most citedGeneralised Network Autoregressive Processes and the GNAR package

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

stat.ME2020

Semi-automated simultaneous predictor selection for Regression-SARIMA models

Aaron Lowther, Paul Fearnhead, Matthew Nunes +1

Deciding which predictors to use plays an integral role in deriving statistical models in a wide range of applications. Motivated by the challenges of predicting events across a te…

stat.ME20192 cited

Generalised Network Autoregressive Processes and the GNAR package

Marina Knight, Kathryn Leeming, Guy Nason +1

This article introduces the GNAR package, which fits, predicts, and simulates from a powerful new class of generalised network autoregressive processes. Such processes consist of a…

stat.AP2019

Interpretable brain age prediction using linear latent variable models of functional connectivity

Ricardo Pio Monti, Alex Gibberd, Sandipan Roy +6

Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we…

stat.AP2018

Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series

Rebecca E. Wilson, Idris A. Eckley, Matthew A. Nunes +1

Distributed acoustic sensing technology is increasingly being used to support production and well management within the oil and gas sector, for example to improve flow monitoring a…

stat.ME2016

Modelling, Detrending and Decorrelation of Network Time Series

M. I. Knight, M. A. Nunes, G. P. Nason

A network time series is a multivariate time series augmented by a graph that describes how variables (or nodes) are connected. We introduce the network autoregressive (integrated)…