Publications
Chan, K. C. G., Ling, H. K., Sit, T., and Yam, S. C. P. (2018). Estimation of a Monotone Density in S-sample Biased Sampling Models. Annals of Statistics, 46(5), 2125-2152.
Cheung, K. C., Ling, H. K., Tang, Q., Yam, S. C. P., and Yuen, F. L. K., (2019). On Additivity of Tail Comonotonic Risks. Scandinavian Actuarial Journal, 10, 837-866.
Chan, K. C. G., Ling, H. K., Sit, T., and Yam, S. C. P. (2021). Grenander-type Nature of Monotone NPMLE of Multi-sample Biased Sampling Models. Electronic Journal of Statistics, 15(1), 2876-2904.
Chan, K. C. G., Ling, H. K., and Yam, S. C. P.(2023) On nonparametric estimation for cross-sectional sampled data under stationarity Electronic Journal of Statistics, 17(2): 2745-2809
Galica J, Saunders S, Pan Z, Silva A, & Ling, H. K. (2024) What do cancer survivors believe caused their cancer? A secondary analysis using the Causes subscale of the Illness Perceptions Questionnaire. Cancer Causes Control 35, 875–886. https://doi.org/10.1007/s10552-023-01846-0
Chu, C. W. and Ling, H.K., Shape-constrained Estimation for Current Duration Data in Cross-sectional Studies. Lifetime Data Analysis
He, Q., Liao, D., Ling, H. K., Jiao, Hong. Evaluating Consistency of Behavioral Patterns across Multiple Tasks Using Process Data: A case study in PIAAC.
Galica, Jacqueline, Stephanie Saunders, Chiamaka Madu, Ziwei Pan, Hok Kan Ling, Jennifer Waite, Denise Neumann-Fuhr, and Erna Snelgrove-Clarke. Registered nurses’ characteristics and their levels of compassion competence and satisfaction: A cross-sectional survey. SAGE Open Nursing 11 (2025)
Under review/ revision
Chen, F, Ling, H. K. and Ying, Z. A Dynamic Factor Model for Multivariate Counting Process Data (arXiv)
Chan, K. C. G., Ling, H. K., Tang, C., and Yam, S. C. P. Likelihood-based Spacings Goodness-of-Fit Statistics for Univariate Shape-constrained Densities (arXiv)
Ke, Y., Ling, H. K., Song, Y. Distance Correlation in Multiple Biased Sampling Models (arXiv)
Chu, C. W., Ling, H. K., Yuan, C. Nonparametric Estimation for a Log-concave Distribution Function with Interval-censored Data (arXiv)
Cheng, P. H., Cohen, J., Ling, H. K., Yam, S. C. P. Generalized Taylor's Law for Dependent and Heterogeneous Heavy-Tailed Data