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

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 …

We propose a novel multivariate nonparametric multiple change point detection method
using classifiers. We construct a classifier log-likelihood ratio that uses class probability …

We propose estimation methods for change points in high-dimensional covariance
structures with an emphasis on challenging scenarios with missing values. We advocate …

We present a unifying view on various statistical estimation techniques including
penalization, variational, and thresholding methods. These estimators are analyzed in the …

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 …

We propose a new method for changepoint estimation in partially observed, high-
dimensional time series that undergo a simultaneous change in mean in a sparse subset of …

For data segmentation in high-dimensional linear regression settings, the regression
parameters are often assumed to be sparse segment-wise, which enables many existing …

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 …

We consider estimation of undirected Gaussian graphical models and inverse covariances
in high-dimensional scenarios by penalizing the corresponding precision matrix. While …