Research is partially supported by NSF funding (DMS - 2515718).
Student / Mentee author *, Corresponding author __
Han, X.*, Chen, K, Yau, C. Y., & Yu, H. (2026+). A simple spectral test for general time series models.
Zhang, Q., Yu, H., Lahiri, S. N., & Nordman, D. J. (2026+). Bootstrap-based nonparametric inference under long-memory time series.
Bera, S.*, Yu, H., Nordman, D. J. & Bandyopadhyay, S. (2026+). Demystifying spectral bias in irregularly spaced spatial data.
Zhou, Y.*, Yu, H. & Nordman, D. J. (2026+). A new frequency domain likelihood inference framework for time series.
Yu, H., Lahiri, S. N. & Nordman, D. J. (2026+). Subsample Aggregation Bootstrap: bridge between bootstrap and subsampling.
Yu, H., Kaiser, M. S., & Nordman, D. J. (2025). A practical interval estimation for spectral density distribution. Journal of the American Statistical Association (Theory and Methods), 338- 350. https://doi.org/10.1080/01621459.2025.2516211
Yu, H., Kaiser, M. S., & Nordman, D. J. (2024). A blockwise empirical likelihood method for time series in frequency domain inference. The Annals of Statistics, 52(3), 1152-1177. DOI: 10.1214/24-AOS2388
Yu, H., Kaiser, M. S., & Nordman, D. J. (2023). A subsampling perspective for extending the validity of state-of-the-art bootstraps in the frequency domain. Biometrika, 110(4), 1099–1115. https://doi.org/10.1093/biomet/asad006
Chan, N.H., Ng, W. L., Yau, C. Y., & Yu. H. (2021). Optimal change-point estimation in time series. The Annals of Statistics, 49(4), 2336–2355. DOI: 10.1214/20-AOS2039 (alphabetical order)
Llana, B., Baron, A., Yu, H., Hosseinpour, M., Suhail, Y., Chin, S., Khadka, K., and Wallace, S. (2026). Reading with Diversity in Mind: Pupillometry and Typography Towards Inclusive Design for ADHD Readers. 2026 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3772363.379938
Yu, H. (2025). Learn R: As a Language, 2nd ed. The American Statistician, 79(3), 417–419. https://doi.org/10.1080/00031305.2025.2490305