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Dr NG Chi Tim, Timothy

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Dr NG Chi Tim, Timothy
吳自添博士

PhD (CUHK)
MPhil (CUHK)
BSc (CUHK)

icosEmail: timothyng@hsu.edu.hk
icosTel: (852) 3963 5172
icosOffice : D626

Assistant Professor

Dr Ng received his BSc in Risk Management Science (Graduated with First Honor), MSc in Risk Management Science, and PhD in Statistics, all from the Chinese University of Hong Kong. Before joining HSUHK, he was a Associate Professor in the Department of Statistics at Chonnam National University in Korea, and a visiting scholar at Department of Economics and Finance, Renmin University in China.

Research Interests Service and Professional Experience Publications Research Grants

Research Interests

  1. Time series analysis
  2. Variable selection
  3. Composite likelihood methods
  4. Stochastic calculus
  5. Option pricing theory

Service and Professional Experience

……….Professional Experience……….

  1. (2019 – ) Associate Editor for Journal of Forecasting
  2. (2019 – ) Associate Editor for Journal of Korean Statistical Society

 

……….Professional Qualifications……….

  1. Passed Society of Actuaries Exams Course 1 to Course 4
  2. Passed Financial Risk Management (FRM) Exam of Global Association of Risk Professionals

Publications

……….Journal Articles……….

  1. Y. Shi and C.T. Ng (2023), A new active zero set descent algorithm for least absolute deviation with generalized LASSO penalty, Journal of Korean Statistical Society, 52, 83–109.
  2. Y. Li, C.T. Ng, and C.Y. Yau (2022), GARCH-type factor model, Journal of Multivariate Analysis, 190, 105001, 42736.
  3. C.T. Ng, W. Lee, and Y. Lee (2022), In defense of LASSO, Communications in Statistics – Theory and Methods, 51, (9), 3018-3042.
  4. K. Zhang, C.T. Ng, Y.M. Kwon, and M.H. Na (2022), New concepts of principal component analysis based on maximum separation of clusters, Communications in Statistics – Simulation and Computation, 51, (5), 2429-2439.
  5. Q.V. Nong and C.T. Ng (2021), Clustering of subsample means based on pairwise L1 regularized empirical likelihood, Annals of the Institute of Statistical Mathematics, 73, 135-174.
  6. C.T. Ng, Y. Shi, and N.H. Chan (2020), Markowitz portfolio and the blur of history, International Journal of Theoretical and Applied Finance, 23, (5), 2050030.
  7. J. Park, C.T. Ng, and M.H. Na (2020), Empirical likelihood method for longitudinal data generated from unequally-spaced Lèvy processes, Journal of Korean Statistical Society, 49, (3), 1008-1025.
  8. K. Zhang, C.T. Ng, and M.H. Na (2020), Real time prediction of irregular periodic time series data, Journal of Forecasting, 39, 501-511.
  9. S. Jeong, J. Ko, M. Kang, J. Yeom, C.T. Ng, S. Lee, Y. Lee, and H. Kim (2020), Geographical variations in gross primary production and evapotranspiration of paddy rice in the Korean Peninsula, Science of the Total Environment, 714, 136632, 1-23.
  10. V.C. Nguyen and C.T. Ng (2020), Variable selection under multi-collinearity using modified log penalty, Journal of Applied Statistics, 47, 201-230.
  11. C.T. Ng, W. Lee, and Y. Lee (2020), Logical and test consistency in pairwise multiple comparisons, Journal of Statistical Planning and Inference, 206, 145-162.
  12. K. Zhang and C.T. Ng (2020), Adaptive LASSO regression against heteroscedastic idiosyncratic factors in the covariates, Statistics and Its Interface, 13, 65-75.
  13. V.C. Nguyen, S. Jeong, J. Ko, C.T. Ng, and J. Yeom (2019), Mathematical integration of remotely-sensed information into a crop modelling process for mapping crop productivity, Remote Sensing, 11, 2131, 1-17.
  14. V.C. Nguyen and C.T. Ng (2019), Removing the singularity of a penalty via thresholding function matching, Journal of Korean Statistical Society, 48, 613-635.
  15. Y. Shi, C.T. Ng, Z. Feng, and C.K.F. Yiu (2019), A descent algorithm for constrained LAD Lasso estimation with applications in portfolio selection, Journal of Applied Statistics, 46, 1988-2009.
  16. Q.V. Nong, C.T. Ng, W. Lee, and Y. Lee (2019), Hypothesis testing via a penalized-likelihood approach, Journal of Korean Statistical Society, 48, 265-277.
  17. C.T. Ng, J. Ko, J. Yeom, S. Jeong, and G. Jeong (2019), Delineation of rice productivity projected via integration of a crop model with geostationary satellite imagery in North Korea, Korean Journal of Remote Sensing, 35, 57-81.
  18. J. Ko, C.T. Ng, S. Jeong, J. Kim, B. Lee, and H. Kim (2019), Impacts of regional climate change on barley yield and its geographical variation in South Korea, International Agrophysics, 33, 91-96.
  19. J. Yeom, S. Jeong, G. Jeong, C.T. Ng, R.C. Deo, and J. Ko (2018), Monitoring paddy productivity in North Korea employing geostationary satellite images integrated with GRAMI-rice model, Scientific Reports, 8, 16121.
  20. Y. Shi, C.T. Ng, and C.K.F. Yiu (2018), Portfolio selection based on asymmetric Laplace distribution, coherent risk measure, and expectation-maximization estimation, Quantitative Finance and Economics, 2, 776-797.
  21. K. Zhang, C.T. Ng, and M. Na (2018), Computational explosion in the frequency estimation of sinusoidal data, Communications for Statistical Applications and Methods, 25, 431-442.
  22. M. Noh, Y. Ok, M. Na, and C.T. Ng (2018), Analysis of degradation data using double hierarchical generalized linear model, Journal of the Korean Data and Information Science Society, 29, 217-228.
  23. J. Choi, J. Ko, C.T. Ng, S. Jeong, J. Tenhunen, W. Xue, and J. Cho (2018), Quantification of CO2 fluxes in paddy rice based on the characterization and simulation of CO2 assimilation approaches, Agricultural and Forest Meteorology, 249, 348-366.
  24. C.T. Ng, W. Lee, and Y. Lee (2018), Change-point estimators with true identification property, Bernoulli, 24, (1), 616-660.
  25. C.T. Ng, C. Li, and X. Fan (2017), A fast algorithm for reconstructing multiple sequence alignment and phylogeny simultaneously, Current Bioinformatics, 12, 329-348.
  26. C.T. Ng and C.Y. Yau (2017), Information criterion of seriously over-fitting change-point models, Statistics and Its Interface, 10, 343-353.
  27. Y. Choi, C.T. Ng, and J. Lim (2017), Regularized LRT for large scale covariance matrices: One sample problem, Journal of Statistical Planning and Inference, 180, 108-123.
  28. C.T. Ng, S. Oh, and Y. Lee (2016), Going beyond oracle property: Selection consistency and uniqueness of local solution of the generalized linear model, Statistical Methodology, 32, 147-160.
  29. C.T. Ng and H. Joe (2016), Comparison of non-nested models under a general measure of distance, Journal of Statistical Planning and Inference, 170, 166-185.
  30. Y. Liu, N.H. Chan, C.T. Ng, and P.S. Wong (2016), Shrinkage estimation of mean-variance portfolio, International Journal of Theoretical and Applied Finance, 19, 1-25.
  31. C.T. Ng and N.H. Chan (2015), Stochastic integral convergence: A white noise calculus approach, Electronic Journal of Statistics, 9, 2035-2057.
  32. C.T. Ng, C.Y. Yau, and N.H. Chan (2015), Likelihood inferences for high-dimensional factor analysis of time series with applications in finance, Journal of Computational and Graphical Statistics, 24, 866-884.
  33. W. Son, C.T. Ng, and J. Lim (2015), A new integral representation of the coverage probability of a random convex hull, Communications for Statistical Applications and Methods, 22, 69-80.
  34. C.T. Ng and C.W. Yu (2014), Modified SCAD penalty for constrained variable selection problems, Statistical Methodology, 21, 109-134.
  35. C.T. Ng, J. Lim, K. Lee, D. Yu, and S. Choi (2014), A fast algorithm to sample the number of vertexes and the area of the random convex hull on the unit square, Computational Statistics, 29, 1187-1205.
  36. C.T. Ng and H. Joe (2014), Model comparison with composite likelihood information criteria, Bernoulli, 20, 1738-1764.
  37. N.H. Chan and C.T. Ng (2011), A note on asymptotic inference for FIGARCH(p,d,q) models, Statistics and Its Inference, 4, 227-233.
  38. S. Lee and C.T. Ng (2011), Normality test for multivariate conditional heteroskedastic dynamic regression models, Economics Letters, 111, (1), 75-77.
  39. C.T. Ng., H. Joe, D. Karlis, and J. Liu (2011), Composite likelihood for time series models with a latent autoregressive process, Statistica Sinica, 21, 279-305.
  40. C.T. Ng, J. Lim, and H. Kyu (2011), Testing stochastic orders in tails of contingency tables, Journal of Applied Statistics, 38, (6), 1133-1149.
  41. H. Joe and C.T. Ng (2010), Generating random AR(p) and MA(q) Toeplitz correlation matrices, Journal of Multivariate Analysis, 101, (6), 1532-1545.
  42. S. Lee and C.T. Ng (2010), Trimmed portmanteau test for linear process with infinite variance, Journal of Multivariate Analysis, 101, (4), 984-998.
  43. N.H. Chan and C.T. Ng (2009), Asymptotic inference for non-stationary GARCH(p,q) models, Electronic Journal of Statistics, 3, 956-992.
  44. N.H. Chan and C.T. Ng (2009), Stochastic integrals driven by fractional Brownian motion and arbitrage, A Tale of Two Integrals, Quantitative Finance, 9, (5), 519-525.
  45. N.H. Chan and C.T. Ng (2006), Fractional constant elasticity of variance model, Time Series and Related Topics, 52, 149-164.

 

……….Conference Papers……….

  1. C.T. Ng and M.H. Na (2015). A note on the fast maximum likelihood estimation algorithm of factor analysis. ISSAT 2015 Proceedings, Vietnam, 62–63 M.H. Na,
  2. C.T. Ng, H.C. Song, and E.H. Hong (2015). Reliability analysis of tires using field data. ISSAT 2015 Proceedings, Vietnam, 74–78
  3. C.T. Ng (2015). High dimensional factor analysis of time series. ITISE 2015 Proceedings, Spain, 733–741 J. Ko and
  4. C.T. Ng (2015). Effective linking of crop modeling and remote sensing. ITISE 2015 Proceedings, Spain, 481–492
  5. H. Joe, J. Qu, C.T. Ng and Y. Lee. (2008). Composite likelihood approach to stochastic volatility models. IASC 2008 Proceedings, Japan, 775–783

Research Grants

  1. (UGC/FDS14/P04/22) HK$1,529,550. “Admixture Analysis of Multi-Site Multivariate Time Series,” funded by the University Grants Committee (UGC) 2022/2023. (PI)
  2. Korea National Research Foundation (Principal Investigator), 2017 – 2020, Real-time prediction of irregular periodic time series data.
  3. Chonnam National University (Principal Investigator), 2017 – 2018, High dimensional variable selection under measurement errors.
  4. Chonnam National University (Principal Investigator), 2014 – 2016, A new penalized likelihood approach of change-point detection.
  5. Korea Rural Development Authority (Co-Investigator), 2017 – 2020, A Study on productivity improvement model and gathering big data of Smart farm in vegetable grown in facilities.
  6. Korea National Research Foundation (Co-Investigator), 2015 – 2018, Data Science and Creation of Knowledge.
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