DMS-EPSRC: Change Point Detection and Localization in High-Dimensions: Theory and Methods
Lead Research Organisation:
University of Warwick
Department Name: Statistics
Abstract
Change point analysis is a well-established topic in statistics that is concerned with detecting and localizing abrupt changes in the data generating distribution in time series and, more broadly, stochastic and spatial processes. A long-studied subject with a rich literature, change point analysis has produced a host of well-established methods for statistical inference available to practitioners. These techniques are widely used in many, diverse applications to address important real life problems, such as security monitoring, neuroimaging, financial trading, ecological statistics, climate change, medical condition monitoring, sensor networks, risk assessment for disease outbreak, flu trend analysis, genetics and many others.
However, existing frameworks for statistical analysis of change point problems often rely on traditional modeling assumptions of parametric nature that are inadequate to capture the inherent complexity of modern, high-dimensional datasets. The broad goal of this proposal is to develop novel theories and methods for change point analysis in high-dimensional and nonparametric settings, for both offline and sequential problems.
However, existing frameworks for statistical analysis of change point problems often rely on traditional modeling assumptions of parametric nature that are inadequate to capture the inherent complexity of modern, high-dimensional datasets. The broad goal of this proposal is to develop novel theories and methods for change point analysis in high-dimensional and nonparametric settings, for both offline and sequential problems.
People |
ORCID iD |
Yi Yu (Principal Investigator) |
Publications
Berrett T B
(2021)
Locally private online change point detection
Carlos Misael Madrid Padilla
(2023)
Change point detection and inference in multivariable nonparametric models under mixing condi- tions
Dubey P
(2021)
Online network change point detection with missing values
Hu Y
(2022)
Graph matching beyond perfectly-overlapping Erdos-Rényi random graphs
in Statistics and Computing
Hu Y
(2022)
Graph matching beyond perfectly-overlapping Erd\H{o}s--R\'enyi random graphs
in Statistics and Computing
Li M
(2021)
Adversarially Robust Change Point Detection
Li M
(2022)
On robustness and local differential privacy
Madrid Padilla O
(2021)
Optimal nonparametric change point analysis
in Electronic Journal of Statistics
Title | changepoints: A Collection of Change-Point Detection Methods |
Description | Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean; 2) univariate polynomials; 3) univariate and multivariate nonparametric settings; 4) high-dimensional covariances; 5) high-dimensional networks with and without missing values; 6) high-dimensional linear regression models; 7) high-dimensional vector autoregressive models; 8) high-dimensional self exciting point processes; 9) dependent dynamic nonparametric random dot product graphs; 10) univariate mean against adversarial attacks. |
Type Of Technology | Software |
Year Produced | 2021 |
Open Source License? | Yes |
Impact | A useful tool for change point detection. |
URL | https://cran.r-project.org/web/packages/changepoints/index.html |