Optimal change point detection and localization in sparse dynamic networks Page 1 The Annals
of Statistics 2021, Vol. 49, No. 1, 203–232 https://doi.org/10.1214/20-AOS1953 © Institute of …

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 …

While many methods are available to detect structural changes in a time series, few
procedures are available to quantify the uncertainty of these estimates post-detection. In this …

Data segmentation aka multiple change point analysis has received considerable attention
due to its importance in time series analysis and signal processing, with applications in a …

Many existing procedures for detecting multiple change-points in data sequences fail in
frequent-change-point scenarios. This article proposes a new change-point detection …

In this article we study the theoretical properties of the simultaneous multiscale change point
estimator (SMUCE) in piecewise‐constant signal models with dependent error processes …

Abstract We propose Narrowest Significance Pursuit (NSP), a general and flexible
methodology for automatically detecting localized regions in data sequences which each …

In recent years, there have been a large number of proposed approaches to detecting
changes in mean. A natural question for an analyst is which method is most appropriate for …

Change points indicate significant shifts in the statistical properties in data streams at some
time points. Detecting change points efficiently and effectively are essential for us to …

We propose a methodology for detecting multiple change points in the mean of an otherwise
stationary, autocorrelated, linear time series. It combines solution path generation based on …