Department of Statistics
University of Manitoba 		 
   
   
 

Education and Experience

    Liqun Wang holds a bachelor's degree in mathematics, a master's degree in statistics and a doctorate in statistics and econometrics. He also has a postgraduate degree in mathematical and computer sciences. Liqun Wang has research and teaching experience at various universities in Europe and North-America. He is an elected member of the International Statistical Institute (ISI).

Research Interests

    Liqun Wang's research interests include boundary crossing problem for Brownian motion and diffusion processes, estimation in nonlinear models with measurement error, high-dimensional variable selection and data assimilation, and Monte Carlo simulation methods in statistical computation and optimization. He is also interested in biostatistics, econometrics, and engineering design and optimizaiton.

Some Selected Papers by Liqun Wang

  1. Wang L (2004). Estimation of nonlinear models with Berkson measurement errors. Annals of Statistics, 32, 2559-2579.
  2. Wang L (2003). Estimation of nonlinear Berkson-type measurement error models. Statistica Sinica, 13, 1201-1210.
  3. Wang L (2007). A unified approach to estimation of nonlinear mixed effects and Berkson measurement error models. Canadian Journal of Statistics, 35, 233-248.
  4. Wang L, Hsiao C (2011). Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models. Journal of Econometrics, 165, 30-44.
  5. Abarin T, Wang L (2012). Instrumental variable approach to covariate measurement error in generalized linear models. Annals of the Institute of Statistical Mathematics, 64, 475-493.
  6. Wang L, Leblanc A (2008). Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 60, 883-900.
  7. Wang L (1998). Estimation of censored linear errors-in-variables models. Journal of Econometrics, 84, 383-400.
  8. Wang L, Hsiao C (1995). A simulated semiparametric estimation of nonlinear errors-in-variables models. Working Paper, Department of Economics, University of Southern California.
  9. Wang L (1990). Generalized shrunken least squares estimators. Chinese Journal of Applied Probability and Statistics, 6, 225-232.
  10. Wang L, Poetzelberger K (1997). Boundary crossing probability for Brownian motion and general boundaries. Journal of Applied Probability, 34, 54-65.
  11. Poetzelberger K, Wang L (2001). Boundary crossing probability for Brownian motion. Journal of Applied Probability, 38, 152-164.
  12. Wang L, Poetzelberger K (2007). Crossing probabilities for diffusion processes with piecewise continuous boundaries. Methodology and Computing in Applied Probability, 9, 21-40.
  13. Fu JC, Wang L (2002). A random-discretization based Monte Carlo sampling method and its application. Methodology and Computing in Applied Probability, 4, 5-25.
  14. Wang L, Shan S, Wang GG (2004). Mode-pursuing sampling method for global optimization on expensive black-box functions. Engineering Optimization, 36, 419-438.
  15. Wang GG, Wang L, Shan S (2005). Reliability assessment using discriminative sampling and metamodeling. SAE Transactions Journal of Passenger Cars - Mechanical Systems, 114 (6), 291-300.
  16. Wu G, Zheng X, Wang L, Zhang S, Liang X, Li Y (2013). A new structure of error covariance matrices and their adaptive estimation in EnKF assimilation. Quarterly Journal of the Royal Meteorological Society, 139, 795-804.
  17. Zhu Q, Wang L, Tannenbaum S, Ricci A, DeFusco P, Hegde P (2014). Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near infrared imaging parameters and tumor pathologic criteria. Breast Cancer Research, 16, 456.

Some Other Recent Publications

  1. Fan J, Wang L, Yan A. (2019). An Inexact Projected Gradient Method for Sparsity-Constrained Quadratic Measurements Regression. Asia-Pacific Journal of Operational Research, 36, 1940008.
  2. Guan J, Cheng H, Bollen KA, Thomas, DR, Wang L. (2019). Instrumental variable estimation in ordinal probit models with mismeasured predictors. Canadian Journal of Statistics, 47, 653-667.
  3. Zhu, Q. et al. (2018). Identifying an early treatment window for predicting breast cancer response to neoadjuvant chemotherapy using immunohistopathology and hemoglobin parameters., Breast Cancer Research, 20:56.
  4. Fan J, Kong L, Wang L, Xiu N. (2018). Variable selection in sparse regression with quadratic measurements. Statistica Sinica, 28, 1157-1178.
  5. Guan J, Wang L. (2017). Instrumental variable estimation in linear quantile regression models with measurement error. Chinese Journal of Applied Probability and Statistics 33, 475-486.
  6. Jin Z, Wang L (2017). First passage time for Brownian motion and piecewise linear boundaries. Methodology and Computing in Applied Probability, 19, 237-253.
  7. Fan J, Kong L, Wang L, Xiu N. (2016). The uniqueness and greedy method for quadratic compressive sensing. 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 808-815.
  8. Li DH, Wang L (2016). A weighted simulation-based estimator for incomplete longitudinal data models. Statistics and Probability Letters, 113, 16-22.
  9. Xu K, Ma Y, Wang L (2015). Instrument assisted regression for errors in variables models with binary response. Scandinavian Journal of Statistics, 42, 104-117.
  10. Zhang S, Zheng X, Chen JM, Chen Z, Dan B, Yi X, Wang L, Wu G. (2015). A global carbon assimilation system using a modified ensemble Kalman filter. Geosci. Model Dev., 8, 805-816.
  11. Abarin T, Li H, Wang L, Briollais L (2014). On method of moments estimation in linear mixed effects models with measurement error on covariates and response with application to a longitudinal study of gene-environment interaction. Statistics in Biosciences, 6, 1-18.
  12. Wu G, Yi X, Wang L, Liang X, Zhang S, Zhang X, Zheng X. (2014). Improving the ensemble transform Kalman filter using a second-order Taylor approximation of the nonlinear observation operator. Nonlinear Processes in Geophysics, 21, 955-970.
  13. Wang L, Lee CH (2014). Discretization-based direct random sample generation. Computational Statistics and Data Analysis, 71, 1001-1010.
  14. Li D, Wang L (2013). A semiparametric estimation approach for linear mixed models. Communications in Statistics - Theory and Methods, 42, 1982-1997.
  15. Li, H., Wang, L (2012). Consistent estimation in generalized linear mixed models with measurement error. Journal of Biometrics and Biostatistics, S7:007, doi:10.4172/2155-6180.S7-007.
liqun.wang@umanitoba.ca    |     Department of Statistics University of Manitoba     |    204 - 474 - 6270