Selected Publication

• 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), 364–373.
  — Reprinted in Taking Food Public: Redfining Foodways in a Changing World. (2011). Editor: P.W. Forson, C. Counihan. Routledge, New York.
• Davis, R.A. & Yau, C.Y. (2011). Comments on pairwise likelihood in time series models. Statistica Sinica, 21(1), 255–278.
• Yau, C.Y. (2012). Empirical likelihood in long-memory time series models. Journal of Time Series Analysis, 33(2), 269–275.
• Loh, J.M. & Yau, C.Y. (2012). A generalization of Neyman-Scott process. Statistica Sinica, 22(4), 1717–1736. 
• 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.
• 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.
• 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.
• 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.
• Chan, N.H., Li, Z. & Yau, C.Y. (2014).  Forecasting online auctions via self‐ exciting point processes. Journal of Forecasting, 33(7), 501–514.
• 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.
• 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.
• Chan, N.H., Yau, C.Y. & Zhang, R.M. (2014).  LASSO estimation of threshold autoregressive models. Journal of Econometrics, 189(2), 285–296.
• 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.
• Chan, K.W. & Yau, C.Y. (2016). New recursive estimators of the time-average variance constant. Statistics and Computing, 26, 609–627.
• 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.
• 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.
• 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.
• Chan, N.H., Wang, M. & Yau, C.Y. (2016). Nonlinear Error Correction Model and Multiple-threshold Cointegration. Statistica Sinica, 26(4), 1479–1499.
• Chan, N.H., Chen, K. & Yau, C.Y. (2016). Bartlett correction of empirical likelihood for non-Gaussian short memory time series. Journal of Time Series Analysis, 37(5), 624–649.
• Chan, N.H., Ing, C.K., Li, Y. & Yau, C.Y. (2017) Threshold Estimation via Group Orthogonal Greedy Algorithm. Journal of Business and Economic Statistics, 35(2), 334–345.
• Leung, S.H., Ng, W.L. & Yau, C.Y. (2017). Sequential change-point detection in time series models based on pairwise likelihood. Statistica Sinica, 27(2), 575–606.
• Ng, C.T. & Yau, C.Y. (2017). Selection of change-point models with Bayesian information criterion. Statistics and its interface, 10(2), 343–353.
• Hui, T.S. and Yau, C.Y. (2017) LARS-type algorithm for Group Lasso Estimation. Statistics and computing, 27(4), 1041–1048.
• Chan, N.H., Lu, Y. & Yau. C.Y. (2017). Factor Modeling for High-dimensional Time Series: Inference and Model Selection. Journal of Time Series Analysis, 38(2), 285–307.
• Chan, K.W. & Yau, C.Y. (2017). Automatic Optimal Batch Size Selection for Recursive Estimators of Time-average Covariance Matrix. Journal of the American Statistical Association, 112, 1076–1089.
• Chan, K.W. & Yau, C.Y. (2017). High order corrected estimator of time-average variance constant. Scandinavian Journal of Statistics, 44, 866–898.
• Gao, Q, Lee, T.C.M. & Yau, C.Y. (2017). Nonparametric Modeling and Break Point Detection for Time Series Signal of Counts. Signal Processing, 138, 307–312.
• Ng, W.L. & Yau, C.Y. (2018). Test for existence of finite moments via bootstrap. Journal of Nonparametric Statistics, 30(1), 28–48.
• Ng, W.L, Yau, C.Y. & Yip, T.C.F. (2018) A Hidden Markov Model for Earthquake Prediction. Stochastic Environmental Research and Risk Assessment, 32(5), 1415–1434. https://link.springer.com/article/10.1007/s00477-017-1457-1
• Chan, N.H., Chen, K., Huang, R. & Yau, C.Y., (2019) Subgroup Analysis of Zero- Inflated Poisson Regression Model with Application to Insurance Data. Insurance: Mathematics and Economics, 86, 8–18. https://doi.org/10.1016/j.insmatheco.2019.01.009
• Li, Y., Yau, C.Y. & Zheng, X (2019) Generalized threshold latent variable model. Electronic Journal of Statistics, 13(1), 2043–2092. https://projecteuclid.org/euclid.ejs/1561168838
• Chen, K, Chan, N.H., Wang, M. & Yau, C.Y. (2019) On Bartlett Correction of Empirical Likelihood for Regularly Spaced Spatial Data. Canadian Journal of Statistics, 47, 455–472. https://onlinelibrary.wiley.com/doi/10.1002/cjs.11508
• Chan, L.H., Chen, K., Li, C., Wong, C.W. & Yau, C.Y. (2019) On Higher Order Moment and Cumulant Estimation. Journal of Statistical Computation and Simulation, 90(4), 747–771. https://www.tandfonline.com/doi/full/10.1080/00949655.2019.1700987 
• Chan, N.H., Ling, S.Q., & Yau, C.Y. (2020) Lasso-based Variable Selection of ARMA Models. Statistica Sinica, 30, 1925-1948. doi:10.5705/ss.202017.0500 
• Chen, K, Chan, N.H. & Yau, C.Y. (2020) Bartlett Correction of Empirical Likelihood with Unknown Variance. Annals of Institute of Statistical Mathematics, 72, 1159–1173. https://doi.org/10.1007/s10463-019-00723-5 
• Chan, N.H., Li, Y., Yau, C.Y. and Zhang, R (2021) Group Orthogonal Greedy Algorithm for Change-point Estimation of Multivariate Time Series. Journal of Statistical Planning and Inference, 212, 14–33. https://doi.org/10.1016/j.jspi.2020.08.002 
• Chan, N.H., Ng, W.L. & Yau, C.Y. (2021) A Self-Normalized Approach to Sequential Change-point Detection for Time Series. To appear in Statistica Sinica.
• Yau, C.Y., Zhu, Z, Loh, J.M. & Lai, S.Y. (2021) Spatial Sampling Design using Generalized Neyman-Scott Process. To appear in Journal of Agricultural, Biological and Environmental Statistics 
• Yau, C.Y. (2021) Factor Modeling for High Dimensional Time Series. To appear in Handbook of Computational Statistics and Data Science.  
• Liu, Z. & Yau, C.Y. (2021) Time Series Analysis for Longitudinal Survey Data Under Informative Sampling and Nonignorable Missingness. To appear in REVSTAT
• Chan, N.H., Ng, W.L, Yau, C.Y., Yu, H. (2021) Optimal Change-point Estimation in Time Series. To appear in Annals of Statistics.
• Ng, W.L, Pan, S. & Yau, C.Y. (2021+) Bootstrap Inference for Multiple Change-points in Time Series. (Submitted)
• Liu, Z. & Yau, C.Y. (2021+) Fitting time series models for longitudinal surveys with nonignorable missing data. (Submitted) 
• Yau, C.Y. & Zhao, Z. (2021+) Alternating Dynamic Programming for Multiple Epidemic Change-Point Estimation. (Submitted) 
• Zhao, Z., Ma, T.F., Ng, W.L. & Yau, C.Y. (2021+) A Composite Likelihood-based Approach for Change-point Detection in Spatio-temporal Process. (Submitted) 
• Chan, K.W. & Yau, C.Y. (2021+). Asymptotically Constant Risk Estimator of Time-average Variance Constant. (Submitted)  
• Liu, Z. & Yau, C.Y. (2021+) A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse. (Submitted) 
• Li, Y., Ng, C.T., & Yau, C.Y. (2021+) GARCH-Type factor model. (Submitted) 
• Chen, X, Ng, W.L. & Yau, C.Y. (2021+) Frequency Domain Bootstrap Methods for Spatial Lattice Data. (Submitted) 
• Ng, W.L. & Yau, C.Y. (2021+) Asymptotic Spectral Theory for Spatial Data. (Submitted)  
• Chen, K., Chan, N.H, Yau, C.Y. and Hu, J. (2021+) Penalized Whittle Likelihood for massive spatial data. (Submitted)