6 papers
Adaptable High-Dimensional Change Point Detection via Ridge Regularization
Haoran Li, Haotian Xu
We study the problem of detecting multiple change points in the mean vectors of an independent sequence of high-dimensional observations. We propose a family of ridge-regularized C…
Online Change Point Detection for Multivariate Inhomogeneous Poisson Processes Time Series
Xiaokai Luo, Haotian Xu, Carlos Misael Madrid Padilla +1
We study online change point detection for multivariate inhomogeneous Poisson point process time series. This setting arises commonly in applications such as earthquake seismology,…
Multivariate Poisson intensity estimation via low-rank tensor decomposition
Haotian Xu, Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla +1
In this work, we propose new matrix- and tensor-based methodologies for estimating multivariate intensity functions of inhomogeneous point processes. By viewing multivariate intens…
Optimal Bias-variance Tradeoff in Matrix and Tensor Estimation
Shivam Kumar, Xiaokai Luo, Haotian Xu +3
We study matrix and tensor denoising when the underlying signal is \textbf{not} necessarily low-rank. In the tensor setting, we observe \[ Y = X^\ast + Z \in \mathbb{R}^{p_1 \times…
Change point localisation and inference in fragmented functional data
Gengyu Xue, Haotian Xu, Yi Yu
We study the problem of change point localisation and inference for sequentially collected fragmented functional data, where each curve is observed only over discrete grids randoml…
Estimation and Inference for Change Points in Functional Regression Time Series
Shivam Kumar, Haotian Xu, Haeran Cho +1
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 R…