activity
20162026
most citedChangepoint analysis of historical battle deaths

21 citations · 39 across the 12 of their papers we have counts for

collaborators

13 papers

stat.ME2026

Multiscale Dynamic Dependence Estimation over Networks

Cristian F. Jiménez-Varón, Marina I. Knight, Matthew A. Nunes

In many settings, observed multivariate time series are often nonstationary in nature, i.e., their second order properties vary over time. An additional feature is that their cross…

stat.ME2025

Network Estimation for Stationary Time Series

Madeline A. Shelley, Chiara Boetti, Marina I. Knight +1

High-dimensional multivariate time series are common in many scientific and industrial applications, where the interest lies in identifying key dependence structure within the data…

stat.ME2025

Long memory network time series

Chiara Boetti, Matthew A. Nunes, Marina I. Knight

Many scientific areas, from computer science to the environmental sciences and finance, give rise to multivariate time series which exhibit long memory, or loosely put, a slow deca…

stat.ME2025

Frequency-Domain Analysis of Time Series with Network-Structured Dependence: Application to Global Bank Connectedness

Cristian F. Jiménez-Varón, Marina I. Knight

Financial spillovers in interconnected systems, such as global banking networks, require tools that capture temporal and frequency dynamics, while incorporating the underlying netw…

stat.ME2024

A multiscale method for data collected from network edges via the line graph

Dingjia Cao, Marina I. Knight, Guy P. Nason

Data collected over networks can be modelled as noisy observations of an unknown function over the nodes of a graph or network structure, fully described by its nodes and their con…

math.ST2023

Continuous Time Locally Stationary Wavelet Processes

Henry Antonio Palasciano, Marina I. Knight, Guy P. Nason

This article introduces the class of continuous time locally stationary wavelet processes. Continuous time models enable us to properly provide scale-based time series models for i…

stat.ME2023

Automatic Locally Stationary Time Series Forecasting with application to predicting U.K. Gross Value Added Time Series under sudden shocks caused by the COVID pandemic

Rebecca Killick, Marina I. Knight, Guy P. Nason +2

Accurate forecasting of the U.K. gross value added (GVA) is fundamental for measuring the growth of the U.K. economy. A common nonstationarity in GVA data, such as the ABML series,…

stat.ME2020★ 2 cited

Multiscale modelling of replicated nonstationary time series

Jonathan Embleton, Marina I. Knight, Hernando Ombao

Within the neurosciences, to observe variability across time in the dynamics of an underlying brain process is neither new nor unexpected. Wavelets are essential in analyzing brain…

math.ST2020★ 14 cited

The Local Partial Autocorrelation Function and Some Applications

Rebecca Killick, Marina I. Knight, Guy P. Nason +1

The classical regular and partial autocorrelation functions are powerful tools for stationary time series modelling and analysis. However, it is increasingly recognized that many t…

stat.ME2019★ 2 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★ 21 cited

Changepoint analysis of historical battle deaths

Brennen T. Fagan, Marina I. Knight, Niall J. MacKay +1

It has been claimed and disputed that World War II has been followed by a `long peace', an unprecedented decline of war. We conduct a full changepoint analysis of well-documented,…

stat.AP2018

Wavelet spectral testing: application to nonstationary circadian rhythms

Jessica Hargreaves, Marina Knight, Jon Pitchford +4

Rhythmic data are ubiquitous in the life sciences. Biologists need reliable statistical tests to identify whether a particular experimental treatment has caused a significant chang…

stat.AP2016

Clustering nonstationary circadian rhythms using locally stationary wavelet representations

Jessica K. Hargreaves, Marina I. Knight, Jon W. Pitchford +1

How does soil pollution affect a plant's circadian clock? Are there any differences between how the clock reacts when exposed to different concentrations of elements of the periodi…