Publications - STA, CUHK

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Journal Articles:

1. 1. 1. 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), 364–373.
        — 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., Wang, M. & Yau, C.Y. (2016). Nonlinear Error Correction Model and Multiple- threshold Cointegration. Statistica Sinica, 26(4), 1479–1499.
      19. 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.
      20. 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.
      21. 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.
      22. Ng, C.T. & Yau, C.Y. (2017). Selection of change-point models with Bayesian information criterion. Statistics and its interface, 10(2), 343–353.
      23. Hui, T.S. and Yau, C.Y. (2017) LARS-type algorithm for Group Lasso Estimation. Statistics and computing, 27(4), 1041–1048.
      24. 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.
      25. 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.
      26. Chan, K.W. & Yau, C.Y. (2017). High order corrected estimator of time-average variance constant. Scandinavian Journal of Statistics, 44, 866–898.
      27. 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.
      28. Ng, W.L. & Yau, C.Y. (2018). Test for existence of finite moments via bootstrap. Journal of Nonparametric Statistics, 30(1), 28–48.
      29. 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
      30. 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
      31. 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
      32. 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
      33. 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
      34. Chan, N.H., Ling, S.Q., & Yau, C.Y. (2020) Lasso-based Variable Selection of ARMA Models. Statistica Sinica, 30, 1925-1948. https://doi:10.5705/ss.202017.0500
      35. 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
      36. 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
      37. Chan, N.H., Ng, W.L. & Yau, C.Y. (2021) A Self-Normalized Approach to Sequential Change- point Detection for Time Series. Statistica Sinica, 31, 491–517. http://www3.stat.sinica.edu.tw/statistica/J31N1/J31N120/J31N120.html
      38. Yau, C.Y., Zhu, Z, Loh, J.M. & Lai, S.Y. (2021) Spatial Sampling Design using Generalized Neyman-Scott Process. Journal of Agricultural, Biological and Environmental Statistics, 26, 105–-127. https://link.springer.com/article/10.1007/s13253-020-00413-3
      39. Yau, C.Y. (2021) Factor Modeling for High Dimensional Time Series. Handbook of Computational Statistics and Data Science. https://doi.org/10.1002/9781118445112.stat08291
      40. Liu, Z. & Yau, C.Y. (2021) Fitting time series models for longitudinal surveys with nonignorable missing data. Journal of Statistical Planning and Inference, 214, 1–12. https://doi.org/10.1016/j.jspi.2021.01.001
      41. Yau, C.Y. & Zhao, Z. (2021) Alternating Dynamic Programming for Multiple Epidemic Change-Point Estimation. Journal of Computational and Graphical Statistics, 30, 808-821. https://doi.org/10.1080/10618600.2020.1868304
      42. Chan, N.H., Ng, W.L, Yau, C.Y., Yu, H. (2021) Optimal Change-point Estimation in Time Series. Annals of Statistics, 49(4) 2336-2355. https://projecteuclid.org › 20-AOS2039 Li, Y., Ng, C.T., & Yau, C.Y. (2022) GARCH-Type factor model. Journal of Multivariate Analysis, 190, 105001. https://doi.org/10.1016/j.jmva.2022.105001
      43. Chen, X, Ng, W.L. & Yau, C.Y. (2022) Frequency Domain Bootstrap Methods for Spatial Lattice Data. Electronic Journal of Statistics, 15, 6586-6632. https://doi.org/10.1214/21-EJS1959
      44. Liu, Z. & Yau, C.Y. (2022) A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse. Statistical Papers, 63, 317-342. https://doi.org/10.1007/s00362-021-01261-0
      45. Ng, W.L, Pan, S. & Yau, C.Y. (2022) Bootstrap Inference for Multiple Change-points in Time Series. Econometrics Theory, 38(4), 752-792. https://doi.org/10.1017/S0266466621000293
      46. Liu, Z. & Yau, C.Y. (2022) Time Series Analysis for Longitudinal Survey Data Under Informative Sampling and Nonignorable Missingness. REVSTAT-Statistical Journal, 20(4), 405–426. https://doi.org/10.57805/revstat.v20i4.379
      47. Chan, N.H., Yau, C.Y. & Zhang, R. (2022) Inference for Structural Breaks in Spatial Models. Statistica Sinica, 32, 1961-1981. https://doi.org/10.5705/ss.202020.0342
      48. Agiwal, V., Kumar, J., Yau, C.Y. (2022) Study of the Trend Pattern of COVID-19 using Spline-Based Time Series Model: A Bayesian Paradigm. Japanese Journal of Statistics and Data Science, 5(1), 363-377. https://pubmed.ncbi.nlm.nih.gov/35425883/ doi:10.1007/s42081-021-00127-x
      49. Ng, W.L. & Yau, C.Y. (2023) Asymptotic spectral theory for spatial data. Stochastics, 95(3), 423-464.
      50. Chen, K., Chan, N.H, Yau, C.Y. and Hu, J. (2023) Penalized Whittle Likelihood for massive spatial data. Journal of Multivariate Analysis, 195, 105156. https://doi.org/10.1016/j.jmva.2023.105156
      51. 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 & Data Analysis, 192, 107886. https://doi.org/10.1016/j.csda.2023.107886
      52. Chen, K., Li, Y., Yau, C.Y., Zhang, X. (2024) Functional Threshold Autoregressive Model, Statistica Sinica, 34, 817-836.
        https://doi.org/10.5705/ss.202022.0096
      53. Yang, B., Tang, X. and Yau, C.Y. (2024) Empirical prediction intervals for additive Holt–Winters methods under misspecification. Journal of Forecasting, 43(3), 754-770. https://doi.org/10.1002/for.3053
      54. Zhao, Z., Ma, T.F., Ng, W.L. & Yau, C.Y. (2024) A Composite Likelihood-based Approach for Change-point Detection in Spatio-temporal Processes. Journal of the American Statistical Association, 119, 3086–3100. https://doi.org/10.1080/01621459.2024.2302200
      55. Chan, K.W. & Yau, C.Y. (2024). Asymptotically Constant Risk Estimator of Time-average Variance Constants. Biometrika, 111(3), 825–842. https://doi.org/10.1093/biomet/asae003
      56. Chan, N.H., Han, C. and Yau, C.Y. (2025) An Extreme-value Test for Structural Breaks in Spatial Trends. Statistica Sinica, 35, 1301-1322. https://doi.org/10.5705/ss.202022.0029
      57. Chan, N.H., Jiao, S. and Yau, C.Y. (2025) Enhanced Structural Break Detection in Functional Means. Statistica Sinica, 35, 873-896. https://doi.org/10.5705/ss.202022.0312
      58. Li, Y., Ng, C.T., Wu, G., Yau, C.Y., Zhang, C. (2025) Change point estimation for high-dimensional time series with network structure
        Electronic Journal of Statistics, 19, 3233–3272. https://doi.org/10.1214/25-EJS2413
      59. Ng, C.T., Yau, C.Y., Li, Y., Qin, L. (2026) Threshold models for high-dimensional time series with network structure. Journal of Multivariate Analysis, 212, 105560. https://doi.org/10.1016/j.jmva.2025.105560
      60. Yuan, G., Yau, C.Y. (2026) Generalized multivariate threshold autoregressive models with linearly partitioned threshold space. Annals of Statistics, 54(4), 2005–2030. http://doi.org/10.1214/26-AOS2638

The Chinese University of Hong Kong

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Statistics and Data Science

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