Many traditional methods for identifying changepoints can struggle in the presence of
outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints to fit …

This paper concerns about the limiting distributions of change point estimators, in a high-
dimensional linear regression time series context, where a regression object $(y_t …

Motivation Next generation sequencing technologies have enabled the study of the human
microbiome through direct sequencing of microbial DNA, resulting in an enormous amount …

Exploiting spatial patterns in large-scale multiple testing promises to improve both power
and interpretability of false discovery rate (FDR) analyses. This article develops a new class …

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 …

In multiple change-point analysis, one of the major challenges is to estimate the number of
change-points. Most existing approaches attempt to minimize a Schwarz information …

Detecting recent changepoints in time-series can be important for short-term prediction, as
we can then base predictions just on the data since the changepoint. In many applications …

In multiple change-point analysis, one of the main difficulties is to determine the number of
change-points. Various consistent selection methods, including the use of Schwarz …

This paper considers a large-scale multiple testing problem for spatiotemporal data with
multiple change points. A data-driven procedure that aims to fully utilize the clustering …

In this paper, the fault detection of a latent fault in a sensor for a Markov jump system is
studied. It is equivalent to detecting a change point in a coefficient vector of a measurement …