High dimensional change point inference: Recent developments and extensions
B Liu, X Zhang, Y Liu - Journal of multivariate analysis, 2022 - Elsevier
Change point analysis aims to detect structural changes in a data sequence. It has always
been an active research area since it was introduced in the 1950s. In modern statistical …
been an active research area since it was introduced in the 1950s. In modern statistical …
A review on minimax rates in change point detection and localisation
Y Yu - arxiv preprint arxiv:2011.01857, 2020 - arxiv.org
This paper reviews recent developments in fundamental limits and optimal algorithms for
change point analysis. We focus on minimax optimal rates in change point detection and …
change point analysis. We focus on minimax optimal rates in change point detection and …
Optimal covariance change point localization in high dimensions
Optimal covariance change point localization in high dimensions Page 1 Bernoulli 27(1), 2021,
554–575 https://doi.org/10.3150/20-BEJ1249 Optimal covariance change point localization in …
554–575 https://doi.org/10.3150/20-BEJ1249 Optimal covariance change point localization in …
Change-point detection for graphical models in the presence of missing values
We propose estimation methods for change points in high-dimensional covariance
structures with an emphasis on challenging scenarios with missing values. We advocate …
structures with an emphasis on challenging scenarios with missing values. We advocate …
Low-Rank Matrix Estimation in the Presence of Change-Points
We consider a general trace regression model with multiple structural changes and propose
a universal approach for simultaneous exact or near-low-rank matrix recovery and change …
a universal approach for simultaneous exact or near-low-rank matrix recovery and change …
Optimal multiple change-point detection for high-dimensional data
E Pilliat, A Carpentier, N Verzelen - Electronic Journal of Statistics, 2023 - projecteuclid.org
This manuscript makes two contributions to the field of change-point detection. In a general
change-point setting, we provide a generic algorithm for aggregating local homogeneity …
change-point setting, we provide a generic algorithm for aggregating local homogeneity …
Variable selection based testing for parameter changes in regression with autoregressive dependence
We consider a regression model with autoregressive terms and propose significance tests
for the detection of change points in this model. Our tests are applicable to both low-or …
for the detection of change points in this model. Our tests are applicable to both low-or …
Efficient Multiple Change Point Detection and Localization For High-Dimensional Quantile Regression with Heteroscedasticity
X Wang, B Liu, X Zhang, Y Liu - Journal of the American Statistical …, 2024 - Taylor & Francis
Data heterogeneity is a challenging issue for modern statistical data analysis. There are
different types of data heterogeneity in practice. In this article, we consider potential …
different types of data heterogeneity in practice. In this article, we consider potential …
Monitoring network changes in social media
Econometricians are increasingly working with high-dimensional networks and their
dynamics. Econometricians, however, are often confronted with unforeseen changes in …
dynamics. Econometricians, however, are often confronted with unforeseen changes in …
Localising change points in piecewise polynomials of general degrees
In this paper we are concerned with a sequence of univariate random variables with
piecewise polynomial means and independent sub-Gaussian noise. The underlying …
piecewise polynomial means and independent sub-Gaussian noise. The underlying …