Kung-Sik Chan

Robert V. Hogg Professor
American Statistical Association Fellow
Institute of Mathematical Statistics Fellow

I started out as a theoretical statistician working on several topics on non-linear time series analysis, including chaos. Through collaborative research with other scientists, I gradually realize and appreciate the use of nonlinear time series analysis and general statistical methods in addressing diverse scientific problems in ecology, epidemiology, hydrology, environmental studies, medical studies, etc. I have been fortunate to have collaborated with many inspiring and dedicated scientists here at Iowa and abroad. Much of my research work originates from attempts to understand and solve interesting scientific problems. At the same time, some of my work focuses on developing new general statistical methodologies. Developing computer packages, for instance in R, is essential to make new methods available to the public. Consequently, together with my students, I have spent substantial efforts in developing several R packages.

Research interests 

  • Data mining
  • High-dimensional inference
  • Medical statistics
  • Fisheries statistics
  • Hydrology
  • Epidemiological modeling
  • Stochastic differential equation modeling
  • Sampling-based inference
  • Chaos
  • Semiparametric statistics

Selected professional development

  • Big Time-Series Analysis, University of Iowa, Iowa City, Iowa, United States, 2019

Selected professional memberships

  • American Statistical Association
  • Institute of Mathematical Statistics
  • International Statistical Institute

Selected awards and honors

  • Fellow of Institute of Mathematical Statistics, 2006
  • Fellow of American Statistical Association, 2001
  • Faculty Scholar Award, University of Iowa, 1996 - 1999
  • Elected Member of International Statistical Institute, 1996

Selected courses taught

  • Time Series Analysis, STAT:7560, Fall 2018
  • Statistical Inference I, STAT:5100, Fall 2018

Selected publications

  • Jarjour, R., Chan, K. (2020). Dynamic Conditional Angular Correlation. Journal of Econometrics, 216(1), 137-150.
  • Chu, Y., Lan, R. S., Huang, R., Feng, H., Kumar, R., Dayal, S., Chan, K. & Dai, D. (2020). Glutathione peroxidase-1 overexpression reduces oxidative stress, and improves pathology and proteome remodeling in the kidneys of old mice. Aging Cell.
  • Chan, K., Goracci, G. (2019). On the ergodicity of first-order threshold autoregressive moving-average processes. Journal of Time Series Analysis, 40(2), 256-264.
  • Wang, C., Chan, K. (2018). Quasi-likelihood estimation of a censored autoregressive model with exogenous variables. Journal of American Statistical Association, 113(523), 1135-1145. DOI: 10.1080/01621459.2017.1307115.
  • Hammond, E., Chan, K., Ames, J. C., Stoyles, N., Sloan, C. M., Guo, J., Newell J., J. D., Hoffman, E. A. & Sieren, J. C. (2018). Impact of Advanced Detector Technology and Iterative Reconstruction on Low Dose Quantitative Assessment of Lung Computed Tomography Density in a Biological Lung Model. Medical physics. PMID: 29926932.
  • Wang, C., Chan, K. (2017). carx: Censored Autoregressive Model with Exogenous Covariates. R package.
  • Thurman, A. L., Choi, J., Choi, S., Lin, C., Hoffman, E. A., Lee, C. H. & Chan, K. (2017). Detection of smoothly distributed spatial outliers, with applications to identifying the distribution of parenchymal hyperinflation following an airway challenge in asthmatics. Statistics in medicine, 36(10), 1638--1654.
  • Chan, K., Hansen, B. E. & Timmermann, A. (2017). Guest Editors' Introduction: Regime Switching and Threshold Models. Taylor \& Francis.
  • Chen, K., Hoffman, E. A., Seetharaman, I., Jiao, F., Lin, C., Chan, K. & others, (2017). Linking lung airway structure to pulmonary function via composite bridge regression. The Annals of Applied Statistics, 10(4), 1880–1906.
  • Jones, C. S., Wang, B., Schilling, K. E. & Chan, K. (2017). Nitrate transport and supply limitations quantified using high-frequency stream monitoring and turning point analysis. Journal of hydrology, 549, 581--591.

Selected presentations

  • Chan, K. (2018, April) Reduced-rank Spectral Classification with High-dimensional Time-Series Data. Conference Presentation presented at Statistics, Monte Carlo, and So Much More: A Conference in Honor of Charlie Geyer, University of Minnesota, Minneapolis, Minnesota.
  • Chan, K., Jarjour, R. (2018, January) Dynamic Conditional Angular Correlation. Invited Lecture presented at Complex Time Series Modelling and Forecasting workshop, Tsinghua University, Sanya, China.

Selected grants and contracts

  • Chan, Kung-Sik (Principal Investigator), Schilling, Keith (Co-Investigator) Contract Research, Applied. Assessment of Ambient Water Quality Data Upstream of Several Iowa Cities. Sponsored by Iowa Department of Natural Resources. Completed. December 1, 2017 - June 30, 2018.
  • Chan, Kung-Sik (Principal Investigator) Grant Industrial Research/Development. Statistical Assessment of Phosphorus Data in Iowa Water,. Sponsored by Iowa Department of Natural Resources. November 20, 2013 - August 30, 2014.
Research areas
  • Data science
  • High-dimensional statistics
  • Markov chain Monte Carlo
  • Statistical ecology
  • Time series analysis
Kung-Sik Chan
PhD, Mathematics, Princeton University
MS, Mathematics, Princeton University
BS, Mathematics, The Chinese University of Hong Kong
Contact Information

University of Iowa
241 Schaeffer Hall (SH)
20 East Washington Street
Iowa City, IA 52240
United States