Change-point inference in high-dimensional regression models under temporal dependence
Page 1 The Annals of Statistics 2024, Vol. 52, No. 3, 999–1026 https://doi.org/10.1214/24-AOS2380 …

This paper addresses the problem of localizing and inferring multiple change points, in non-
parametric multivariate time series settings. Specifically, we consider a multivariate time …

In this paper, we study the offline change point localization problem in a sequence of
dependent nonparametric random dot product graphs. To be specific, assume that at every …

This article focuses on detecting change points in high‐dimensional linear regression
models with piecewise constant regression coefficients, moving beyond the conventional …

Modern time series data often exhibit complex dependence and structural changes which
are not easily characterised by shifts in the mean or model parameters. We propose a …

In this paper, we study the estimation and inference of change points under a functional
linear regression model with changes in the slope function. We present a novel Functional …

Changepoint detection is an important problem with a wide range of applications. There are
many different types of changes that one may wish to detect, and a widerange of algorithms …

In this paper, we study the offline change point localization problem in a sequence of
dependent nonparametric random dot product graphs. To be specific, assume that at every …

In this paper we study online change point detection in dynamic networks with time
heterogeneous missing pattern within networks and dependence across the time course …

Confidence intervals are constructed for multiple change points in high-dimensional linear
regression models. Locally refitted estimators are developed, and their rate of convergence …