We introduce a new estimator, the simultaneous multiscale change point estimator SMUCE,
for the change point problem in exponential family regression. An unknown step function is …

We propose seeded binary segmentation for large-scale changepoint detection problems.
We construct a deterministic set of background intervals, called seeded intervals, in which …

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 …

We propose, a heterogeneous simultaneous multiscale change point estimator called 'H-
SMUCE'for the detection of multiple change points of the signal in a heterogeneous …

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 …

Fast multiple change-point segmentation methods, which additionally provide faithful
statistical statements on the number, locations and sizes of the segments, have recently …

Supplement to “Tail-greedy bottom-up data decompositions and fast multiple change-point
detection”. Extension of the TGUH methodology to dependent non-Gaussian data; …

Since the universal acceptance of atoms and molecules as the fundamental constituents of
matter in the early-twentieth century, molecular physics, chemistry, and molecular biology …

To segment a sequence of independent random variables at an unknown number of change-
points, we introduce new procedures that are based on thresholding the likelihood ratio …

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur
over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as …