R package · GitHub
FragmentCP
Change point localisation and inference in fragmented functional data.
Haotian Xu · Auburn University
Research
Publications
15 papersForthcoming
Multivariate Poisson intensity estimation via low-rank tensor decomposition
Journal of the American Statistical Association
To appear, 2026Estimation and inference of change points in functional regression time series
Statistica Sinica
Forthcoming
2026
Non-asymptotic analysis of median-of-means estimation for high-dimensional time series
Statistical Learning and Data Science
Article 100008, 2026Online change point detection for multivariate inhomogeneous Poisson processes time series
International Conference on Machine Learning (ICML)
2026Covariance change point localisation and inference in fragmented functional data
Bernoulli
32(2), 1456–1480, 2026Online network change point detection with missing values and temporal dependence
Journal of Time Series Analysis
47(3), 687–700, 2026
2024
Change point inference in high-dimensional regression models under temporal dependence
The Annals of Statistics
52(3), 999–1026, 2024
2023
Change point detection and inference in multivariate non-parametric models under mixing conditions
Advances in Neural Information Processing Systems (NeurIPS)
36, 21081–21134, 2023
2022
Localising change points in piecewise polynomials of general degrees
Electronic Journal of Statistics
16(1), 1855–1890, 2022Robust two-step wavelet-based inference for time series models
Journal of the American Statistical Association
117(540), 1996–2013, 2022
2020
Wavelet-based moment-matching techniques for inertial sensor calibration
IEEE Transactions on Instrumentation and Measurement
69(10), 7542–7551, 2020
2019
Multivariate signal modeling with applications to inertial sensor calibration
IEEE Transactions on Signal Processing
67(19), 5143–5152, 2019
2018
Is nonmetastatic cutaneous melanoma predictable through genomic biomarkers?
Melanoma Research
28(1), 21–29, 2018An optimal virtual inertial sensor framework using wavelet cross covariance
IEEE/ION Position, Location and Navigation Symposium (PLANS)
1342–1350, 2018
2017
A study of the Allan variance for constant-mean nonstationary processes
IEEE Signal Processing Letters
24(8), 1257–1260, 2017
Preprints & working papers
8 papersOptimal detection of general moment changes: Simultaneous mean and covariance change detection and beyond
2026 · Preprint
On tail-robust autocovariance matrix estimation for high-dimensional and potentially nonstationary time series
2026 · Preprint
Towards open science: Monitoring crustal deformations in North America
2026 · Preprint
Adaptable high-dimensional change point detection via ridge regularization
2026 · Preprint
Online learning for autoregressive multilayer stochastic block models under stationarity and non-stationarity
2026 · Preprint
Optimal bias-variance tradeoff in matrix and tensor estimation
2025 · Revised 2026
Inference for large scale regression models with dependent errors
2024 · Preprint
Multiple change points detection problems for high-dimensional time series
Submitted manuscript
Statistical software
R package · CRAN
changepoints
Offline and online change point detection algorithms across a range of statistical settings.
R package · GitHub
rcov
Robust autocovariance matrix estimation for high-dimensional time series.
R package · CRAN
avar
Allan variance and Allan variance linear regression estimation for time series models.