Liqun Diao

Research Assistant Professor
Liqun Diao

Contact Information:
Liqun Diao

Personal Website

Research Interests

I am interested in developing and applying data-driven statistical methods and machine learning algorithms to advance knowledge in fields including medicine, public health, and insurance. I have been working on a broad spectrum of areas including recursive partitioning learning, causal inference, dependence modelling, Bayesian methods, and two-phase design.

Education/biography

  • 2013 Ph.D. in Statistics - Biostatistics, University of Waterloo, Waterloo, Canada
  • 2009 M.Math in  Statistics - Biostatistics, University of Waterloo, Waterloo, Canada
  • 2007 B.Econ in Statistics, Renmin University of China, Beijing, China

Professor Diao has joined the Department of Statistics and Actuarial Science at the University of Waterloo since July, 2015 as an assistant professor.

Selected Publications

  • Yang, C., Cook, R.J., Diao, L., 2021+. Secondary Analysis and Sequential Design of Two-Phase Studies. Under Revision for Statistical Methods in Medical Research.    
  • Diao, L., Meng, Y., Weng, C., Wirjanto, T., 2021+. Common Mortality Trend Model and Mortality Prediction. Under Revision for North American Actuarial Journal. 
  • Diao, L., Yi, Y., 2021+. Classification Trees for Misclassified Responses. Under Revision for Journal of Classification.    
  • Yang, C., Diao, L., Cook, R.J., 2021+. Regression Trees for Interval-censored Failure Time Data Based on Censoring Unbiased Transformations and Pseudo-Observations. Under Revision for Canadian Journal of Statistics.
  • Yang, C., Cook, R.J., Diao, L., 2021+. Adaptive Two-Phase Designs: Some Results on Robustness and Efficiency. Revision Submitted to Statistics in Medicine.  
  • Zhuang, H., Diao, L., Yi, Y., 2021+. Polya Tree Based Nearest Neighbour Regression. Revision Submitted to Statistics and Computing.  
  • Cuerden, M., Diao, L., Cotton, C., Cook, R.J., 2021+. Multiple Imputation and Doubly Weighted Estimating Equations for Causal Inference with Incomplete Subgroup Data. Revision Submitted to Biostatistics and Epidemiology.
  • Zhuang, H., Diao, L., Yi, Y., 2021. A Bayesian Nonparametric Mixture Model for Grouping Dependence Structures and Selecting Copula Functions. Econometrics and Statistics (In Press).  
  • Diao, L. Cook, R.J., 2021. Nested Doubly Robust Estimating Equations for Causal Analysis with an Incomplete Effect Modifier. Canadian Journal of Statistics (In Press).
  • Yang, C., Diao, L., Cook, R.J., 2021. Survival Trees for Current Status Data. Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications, Proceedings of Machine Learning Research 146, 83-94  
  • Zhuang, H., Diao, L., Yi, Y., 2021. A Vine Copula Model for Climate Trend Analysis using Canadian Temperature Data. Journal of Data Science. 19(1) 37–55.  
  • Diao, L., Meng, Y., Weng, C., 2021. A DSA Algorithm for Mortality Forecasting. North American Actuarial Journal. 25(3) 438-458 
  • Zhuang, H., Diao, L., Yi, Y., 2020. A Bayesian Hierarchical Copula Model. Electronic Journal of Statistics. 14(2), 4457-4488.
  • Steingrimsson, J.A.∗, Diao, L.*, Strawderman, R.L., 2019. Censoring Unbiased Regression Trees and Ensembles. Journal of the American Statistical Association 114(525), 370-383. 
  • Diao, L. and Weng, C., 2019. Regression Tree Credibility Model. North American Actuarial Journal 23(2), 169-196.
  • Steingrimsson, J.A., Diao, L., Molinaro, A.M., Strawderman, R.L., 2016. Double Robust Survival Trees. Statistics in Medicine 35(20), 3595-3612.   
  • Diao, L. and Cook, R.J., 2014. Composite Likelihood for Joint Analysis of Multiple Multistate Processes via Copulas. Biostatistics 15(4), 690-705. 
  • Diao, L., Cook, R.J. and Lee, K.-A., 2013. A Copula Model for Marked Point Processes. Lifetime Data Analysis 19(4), 463-489.