B.Sc. (HKU)             2004

M.Phil. (CUHK)        2006 

Ph.D. (Columbia)    2010

1. Kwate, N.O.,  Yau, C.Y., Loh J.M., & Williams D. (2009). Inequality in obesigenic environments: fast food density in New York City, Health Place 15 (1), 364-73.
   --- Reprinted in Taking Food Public: Redfining Foodways in a Changing World. (2011). Editor: P.W. Forson, C. Counihan. Routledge, New York.
2. Davis, R.A. & Yau, C.Y. (2011). Comments on pairwise likelihood in time series models. Statistica Sinica, 21(1), 255-278.
3. Yau, C.Y. (2012). Empirical likelihood in long-memory time series models. Journal of Time Series Analysis, 33(2), 269-275.
4. Loh, J.M. & Yau, C.Y. (2012). A generalization of Neyman-Scott process. Statistica Sinica, 22(4), 1717-1736. 
5. Davis, R.A. & Yau, C.Y. (2012). Likelihood Inference for discriminating between long-range dependence and change-point models. Journal of Time Series Analysis, 33(4), 649-664.
6. Davis, R.A. & Yau, C.Y. (2013). Consistency of minimum description length model selection for piecewise stationary times series models. Electronic Journal of Statistics, 7, 381-411.
7. Chan, N.H., Yau, C.Y. & Zhang, R.M. (2014).  Group LASSO for structural break time series. Journal of the American Statistical Association, 109, 590-599.
8. Yau, C.Y. (2014). Discussion on "Multiscale change point inference" by Frick, K., Munk, A. and Sieling, H. Journal of the Royal Statistical Society - Series B. 76, 565-566.
9. Chan, N.H., Li, Z. & Yau, C.Y. (2014).  Forecasting online auctions via self‐ exciting point processes. Journal of Forecasting, 33(7), 501-514.
10. Chan, N.H., Chen, K. & Yau, C.Y. (2014). On the Bartlett correction of empirical likelihood in Gaussian long-memory time series. Electronic Journal of  Statistics, 8, 1460-1490.
11. Chan, N.H., Ng, C.T. & Yau, C.Y. (2014). Likelihood inferences for high dimensional dynamic factor analysis with applications in finance. Journal of Computational and Graphical Statistics, 24(3), 866-884.
12. Chan, N.H., Yau, C.Y. & Zhang, R.M. (2014).  LASSO estimation of threshold autoregressive models. Journal of Econometrics, 189(2), 285–296.
13. Lee, T.C.M., Tang, C.M. & Yau, C.Y. (2015). Estimation of multiple-regime threshold autoregressive models with structural breaks. Journal of the American  Statistical Association, 110, 1175-1186.
14. Chan, K.W. & Yau, C.Y. (2016). New recursive estimators of the time-average variance constant. Statistics and Computing, 26, 609-627.
15. Ma, T.F. & Yau, C.Y. (2016). A pairwise likelihood-based approach for change- point detection in multivariate time series models. Biometrika, 103(2), 409-421.
16. Wu, C., Wang, M.H., Lu, X., Chong, K.C.,  He, J., Yau, C.Y., Hui, M., Cheng, X, Yang, L., Zee, B.C.Y., Zhang R., He, M.L. (2016) Concurrent epidemics of influenza A/H3N2 and A/H1N1pdm in Southern China: A serial cross-sectional study. Journal of Infection, 72, 369-376.
17. Yau, C.Y. & Zhao Z. (2016). Inference for multiple change-points in time series via likelihood ratio scan statistics. Journal of the Royal Statistical Society - Series B, 78(4), 895-916.
18. Chan, N.H., Ing, C.K., Li, Y. & Yau, C.Y. (2016+) Threshold Estimation via Group Orthogonal Greedy Algorithm. (To appear in Journal of Business and Economic Statistics)
19. Chan, N.H., Wang, M. & Yau, C.Y. (2016+). Nonlinear Error Correction Model and Multiple-threshold Cointegration. (To appear in Statistica Sinica)
20. Chan, N.H., Chen, K. & Yau, C.Y. (2016+). Bartlett correction of empirical likelihood for non-Gaussian short memory time series. (To appear in Journal of Time Series Analysis)
21. Leung, S.H., Ng, W.L. & Yau, C.Y. (2016+). Sequential change-point detection in time series models based on pairwise likelihood. (To appear in Statistica Sinica)
22. Ng, C.T. & Yau, C.Y. (2016+). Selection of change-point models with Bayesian information criterion. (To appear in Statistics and its interface)
23. Hui, T.S. and Yau, C.Y. (2016+) LARS-type algorithm for Group Lasso Estimation. (To appear in Statistics and computing)
24. Chan, N.H., Lu, Y. & Yau. C.Y. (2016+). Factor Modeling for High-dimensional Time Series: Inference and Model Selection. (To appear in Journal of Time Series Analysis)
25. Chan, K.W. & Yau, C.Y. (2016+). Automatic Optimal Batch Size Selection for Recursive Estimators of Time-average Covariance Matrix. (To appear in Journal of the American Statistical Association)
26. Ng, W.L. & Yau, C.Y. (2016+). Test for existence of finite moments via bootstrap. (Submitted)
27. Chan, K.W. & Yau, C.Y. (2016+). High order corrected estimator of time-average variance constant. (Submitted)
28. Yau, C.Y. (2016+). Testing nonnested hypothesis via composite likelihood. (Submitted)
29. Ng, W.L, Pan, S., Yau, C.Y.(2016+) Bootstrap Inference for Multiple Change- points in Time Series. (Submitted)
30. Yau, C.Y., Zhu, Z, Loh, J.M. & Lai, S.Y. (2016+) Spatial Sampling Design using Generalized Neyman-Scott Process. (Submitted)
31. Chen, K, Chan, N.H., Yau, C.Y. (2016+) Bartlett Correction of Empirical Likelihood with Unknown Variance. (Submitted)

RMSC1101 Elementary Concepts in Risk Management
RMSC4005 Stochastic Calculus for Finance and Risk
STAT4005 Time Series
STAT6104 Financial Time Series

RMSC Program Orientation 2016 (pdf)