Dr NG Wai Leong, Tom

1. Change-point Analysis
2. Bootstrap Resampling Method
3. Time Series

……….Journal Articles……….

1. Zhao, Z., Ma, T. F., Ng, W. L. and Yau, C. Y. (2024+). A Composite Likelihood-based Approach for Change-point Detection in Spatio-temporal Processes. (To appear in Journal of the American Statistical Association – Theory and Methods).
2. Li, Y., Chan, C. K., Yau, C. Y., Ng, W. L. and Lam, H. (2024). Burn-in Selection in Simulating Stationary Time Series. Computational Statistics and Data Analysis, 192, 107886.
3. Ng, W. L. and Yau, C. Y. (2023). Asymptotic Spectral Theory for Spatial Data. Stochastics, 95(3), 423-464.
4. Ng, W. L., Pan, S. and Yau, C. Y. (2022). Bootstrap Inference for Multiple Change-points in Time Series. Econometric Theory, 38(4), 752-792.
5. Ng, W. L., Yau, C. Y. and Chen, X. (2021). Frequency Domain Bootstrap Methods for Random Fields. Electronic Journal of Statistics, 15(2), 6586-6632. DOI: 10.1214/21-EJS1959.
6. Chan, N. H., Ng, W. L., Yau, C. Y. and Yu, H. (2021). Optimal Change-point Estimation in Time Series. Annals of Statistics, 49(4), 2336-2355. DOI: 10.1214/20-AOS2039.
7. Chan, N. H., Ng, W. L., and Yau, C. Y. (2021). A Self-Normalized Approach to Sequential Change-point Detection for Time Series. Statistica Sinica, 31(1), 491-517. DOI: 10.5705/ss.202018.0269.
8. Ng, W. L. and Yau, C. Y. (2018). Test for Existence of Finite Moments via Bootstrap. Journal of Nonparametric Statistics, 30(1), 28-48.
9. Yip, T. C. F., Ng, W. L. and Yau, C. Y. (2018). A Hidden Markov Model for Earthquake Prediction. Stochastic Environmental Research and Risk Assessment, 32, 1415-1434.
10. Leung, S. H., Ng, W. L. and Yau, C. Y. (2017). Sequential Change-point Detection in Time Series Models based on Pairwise Likelihood. Statistica Sinica, 27(2), 575-606.

……….FDS……….

1. (UGC/FDS14/P01/24) HK$670,550. “Multiple Change-point Detection in Spatio-temporal Gaussian Random Fields under Spatial Fixed-domain and Mixed-domain Asymptotic Frameworks,” funded by the University Grants Committee (UGC) 2024/2025. (PI)
2. (UGC/FDS14/P05/22) HK$1,324,850. “Generalized Fiducial Inference and Model Selection on Multiple Change-point Detection in Autoregressive Time Series,” funded by the University Grants Committee (UGC) 2022/2023. (PI)
3. (UGC/FDS14/P04/21) HK$973,000. “Sequential Change-Point Detection in High-Dimensional Vector Autoregressive Models,” funded by the University Grants Committee (UGC) 2021/2022. (PI)
4. (UGC/FDS14/P01/20) HK$1,628,450 “Inference for Multiple Change-points in Piecewise Locally Stationary Time Series,” funded by the University Grants Committee (UGC) 2020/2021. (PI)