Peisong Han

Assistant Professor

Peisong HanContact Information:
Peisong Han


Research interests

My research interests focus on the development of new methodologies for statistical analysis with missing data along the line of semiparametric approaches, where only certain moments of the data distribution are specified. The main statistical tool I use is the empirical likelihood, which, despite its nonparametric nature, possesses many desirable properties similar to those of a parametric likelihood.

One aim of my current research is to obtain estimators whose consistency is highly robust against model misspecifications, the so-called multiply robust estimators, for regression analysis with different types of data, e.g. cross-sectional data and longitudinal/correlated data. This aim will significantly generalize the method in my Biometrika paper (Han and Wang, 2013) to a more widely adopted regression setting in statistical and biostatistical research. Another aim is to improve the estimation efficiency of the multiply robust estimators, so that, in addition to attaining the semiparametric efficiency bound in the “ideal” case, these estimators will have high efficiency in “non-ideal” cases. One bonus of using these multiply robust estimators is that, they are not sensitive to extreme values of the estimated missingness probability, in contrast to the existing estimators based on the inverse probability weighting method.

My other research interests include empirical likelihood theory, estimating functions theory, semiparametric efficiency theory, and longitudinal/correlated/clustered data analysis. 


  • 2013 PhD Biostatistics, University of Michigan, Ann Arbor, U.S.A.
  • 2008 MS Statistics, Michigan State University, East Lansing, U.S.A.
  • 2006 BS Mathematics, University of Science and Technology of China, Hefei, China

Professor Han has joined the Department of Statistics and Actuarial Science at the University of Waterloo since August, 2013 as an assistant professor.

Selected publications

  • Han, P. (2017). Calibration and Multiple Robustness When Data Are Missing Not At Random. Statistica Sinica. Accepted.

  • Han, P. (2016). Intrinsic Efficiency and Multiple Robustness in Longitudinal Studies with Dropout. Biometrika, 103, 683-700.

  • Han, P. and Lawless, J. F. (2016). Discussion of "Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-level Information from External Big Data Source" by Chatterjee, Chen, Maas and Carroll. Journal of the American Statistical Association, 111, 118-121.

  • Han, P. (2016). Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation. Scandinavian Journal of Statistics, 43, 246-260.

  • Han, P., Song, P. and Wang, L. (2015). Achieving Semiparametric Efficiency Bound in Longitudinal Data Analysis with Dropouts. Journal of Multivariate Analysis, 135, 59-70.
  • Han, P. (2014). Multiply Robust Estimation in Regression Analysis with Missing Data. Journal of the American Statistical Association, 109, 1159-1173. (This paper won the 2014 David P. Byar Young Investigator Award from Biometrics Section of the American Statistical Association.)
  • Han, P. (2014). A Further Study of the Multiply Robust Estimator in Missing Data Analysis. Journal of Statistical Planning and Inference, 148, 101-110.
  • Han, P. and Wang, L. (2013). Estimation with Missing Data: Beyond Double Robustness. Biometrika, 100 (2), 417-430.
  • Han, P. (2012). A Note on Improving the Efficiency of Inverse Probability Weighted Estimator Using the Augmentation Term. Statistics and Probability Letters, 82 (12), 2221-2228.
University of Waterloo
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