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
Yu Y
(2021)
Optimal network online change point localisation
Li M
(2021)
Adversarially Robust Change Point Detection
Berrett T B
(2021)
Locally private online change point detection
Manegueu A G
(2021)
Generalized non-stationary bandits
Rinaldo A
(2021)
Localizing Changes in High-Dimensional Regression Models
Dubey P
(2021)
Online network change point detection with missing values
Wang D
(2021)
Optimal covariance change point localization in high dimensions
in Bernoulli
Padilla O H M
(2021)
Lattice partition recovery with dyadic CART
Padilla O.H.M.
(2021)
Lattice partition recovery with dyadic CART
in Advances in Neural Information Processing Systems
Madrid Padilla O
(2021)
Optimal nonparametric change point analysis
in Electronic Journal of Statistics
Yu Y
(2022)
Optimal partition recovery in general graphs
Padilla Oscar Hernan Madrid
(2022)
Change point localization in dependent dynamic nonparametric random dot product graphs
in JOURNAL OF MACHINE LEARNING RESEARCH
Li M
(2022)
On robustness and local differential privacy
Madrid Padilla O
(2022)
Optimal Nonparametric Multivariate Change Point Detection and Localization
in IEEE Transactions on Information Theory
Hu Y
(2022)
Graph matching beyond perfectly-overlapping Erd\H{o}s--R\'enyi random graphs
in Statistics and Computing
Yu Y
(2022)
Localising change points in piecewise polynomials of general degrees
in Electronic Journal of Statistics
Hu Y
(2022)
Graph matching beyond perfectly-overlapping Erdos-Rényi random graphs
in Statistics and Computing
Padilla O.H.M.
(2022)
Change point localization in dependent dynamic nonparametric random dot product graphs
in Journal of Machine Learning Research
Yu Y
(2022)
Localising change points in piecewise polynomials of general degrees
in Electronic Journal of Statistics
Madrid Padilla, O. H.
(2022)
Change point localization in dependent dynamic nonparametric random dot product graphs
in Journal of Machine Learning Research
Madrid Padilla, O. H.
(2022)
Dynamic and heterogeneous treatment effects with abrupt changes
Carlos Misael Madrid Padilla
(2023)
Change point detection and inference in multivariable nonparametric models under mixing condi- tions
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 |