I am an assistant professor in the Department of Mathematics and Statistics at Queen's University. I received my Ph.D. in Statistics from Columbia University in 2020.
Research interests:
Nonparametric and semiparametric inference; shape-constrained inference; survival analysis and event history analysis for biased sampling, cross-sectional sampling, censoring, recurrent events, and longitudinal data; dependence measures and heavy-tailed data.
Courses Taught
STAT 268: Statistics and Probability I
Fall 2020, Fall 2021
STAT 269: Statistics and Probability II
Winter 2021, Winter 2022, Winter 2023, Winter 2026
STAT 362: R for Data Science
Winter 2021, Winter 2022, Winter 2023, Winter 2024, Winter 2025, Winter 2026
STAT 462 / STAT 862: Statistical Learning I
Fall 2022
STAT 457 / MTHE 457 / STAT 857: Statistical Learning II
Winter 2024, Winter 2025, Winter 2027 scheduled
MATH 895: Mathematical Statistics I
Fall 2023, Fall 2024, Fall 2025, Fall 2026 scheduled
STAT 965: Topics in Statistics
Fall 2022
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). On asymptotic equivalence of the NPMLE of a monotone density and a Grenander-type estimator in 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.
Chu, C. W. and Ling, H.K. (2025) Shape-constrained Estimation for Current Duration Data in Cross-sectional Studies. Lifetime Data Analysis, 31(3), 595-630.
Galica, J., Saunders, S., Madu, C., Pan, Z., Ling, H. K., Waite, J., Neumann-Fuhr, D., and Snelgrove-Clarke, E. (2025). Registered nurses’ characteristics and their levels of compassion competence and satisfaction: A cross-sectional survey. SAGE Open Nursing, 11, 23779608251367257.
Chu, C. W., Ling, H. K., Yuan, C. (2026) Nonparametric estimation for a log-concave distribution function with interval-censored data Electronic Journal of Statistics, 20(1), 2531-2585.
Software: iclogcondist, an R package for nonparametric estimation of log-concave distribution functions with interval-censored data.
Chan, K. C. G., Ling, H. K., Tang, C., and Yam, S. C. P. (to appear) Likelihood-based Spacings Goodness-of-Fit Statistics for Univariate Shape-constrained Densities . Bernoulli.
Cheng, P. H., Cohen, J., Ling, H. K., Yam, S. C. P. (to appear) Generalized Taylor's Law for Infinite-Mean Heavy-Tailed Data Under Dependence and Heterogeneity Proceedings of the National Academy of Sciences of the United States of America
Under review/ revision
Chen, F, Ling, H. K. and Ying, Z. A Dynamic Factor Model for Multivariate Counting Process Data (arXiv)
Ke, Y., Ling, H. K., Song, Y. Distance Correlation in Multiple Biased Sampling Models (arXiv)
Chan, K. C. G., Ling, H. K., Sun, Z., and Yam, S. C. P. Maximum mean discrepancy based on derivatives of characteristic function.
Ling, H. K. and Yam, S. C. P. Optimality and sharpness of uniform rates for strong laws of large numbers.
Ke, Y., Ling, H. K., and Tu, D. A partially linear additive single-index threshold model for classification of patients into treatment-sensitive subgroups based on longitudinal interval-bounded data.