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 has been an editor, associate editor and editorial board member of a number of journals.

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 and econometrics.

Some Selected Papers by Liqun Wang

  1. Wang L, Poetzelberger K. (1997). Boundary crossing probability for Brownian motion and general boundaries. Journal of Applied Probability, 34, 54-65.
  2. Poetzelberger K, Wang L. (2001). Boundary crossing probability for Brownian motion. Journal of Applied Probability, 38, 152-164.
  3. Wang L, Poetzelberger K. (2007). Crossing probabilities for diffusion processes with piecewise continuous boundaries. Methodology and Computing in Applied Probability, 9, 21-40.
  4. Wang L. (2004). Estimation of nonlinear models with Berkson measurement errors. Annals of Statistics, 32, 2559-2579.
  5. Wang L. (2003). Estimation of nonlinear Berkson-type measurement error models. Statistica Sinica, 13, 1201-1210.
  6. Wang L. (2007). A unified approach to estimation of nonlinear mixed effects and Berkson measurement error models. Canadian Journal of Statistics, 35, 233-248.
  7. Wang L, Hsiao C. (2011). Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models. Journal of Econometrics, 165, 30-44.
  8. Wang L and Hsiao C. (1996). A semiparametric estimation of nonlinear errors-in-variables models. Proceedings of the Business and Economic Statistics Section, pp. 231-236, American Statistical Association.
  9. Wang L. (1998). Estimation of censored linear errors-in-variables models. Journal of Econometrics, 84, 383-400.
  10. Wang L, Leblanc A. (2008). Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 60, 883-900.
  11. 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.
  12. Li, H.; Wang, L. (2012). A consistent simulation-based estimator in generalized linear mixed models. Journal of Statistical Computation and Simulation, 82, 1085-1103.
  13. Wang L, Lee CH. (2014). Discretization-based direct random sample generation. Computational Statistics and Data Analysis, 71, 1001-1010.
  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. 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.

Some Other Recent Publications

  1. Kandic M, Fernando IT, Gole AM, Wang L. (2021). Incorporating multi-year asset replacement time into calculation of asset's expected annual unavailability due to end-of-life failure. IEEE Transactions on Power Systems.
  2. Wang L. (2021). Identifiability in measurement error models. In G.Y. Yi, A. Delaigle and P. Gustafson (Eds.), Handbook of Measurement Error Models, Chapter 3, Chapman & Hall/CRC.
  3. Wang L. (2021). Estimation in mixed-effects models with measurement error. In G. Y. Yi, A. Delaigle and P. Gustafson (Eds.), Handbook of Measurement Error Models, Chapter 17, Chapman & Hall/CRC.
  4. Jiang J, Wang L, Wang L. (2021). Linear approximate Bayes estimator for regression parameter with an inequality constraint. Communications in Statistics - Theory and Methods.
  5. 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.
  6. 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.
  7. 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.
  8. Fan J, Kong L, Wang L, Xiu N. (2018). Variable selection in sparse regression with quadratic measurements. Statistica Sinica, 28, 1157-1178.
  9. Jin Z, Wang L (2017). First passage time for Brownian motion and piecewise linear boundaries. Methodology and Computing in Applied Probability, 19, 237-253.
  10. 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.
  11. Fan J, Kong L, Wang L, Xiu N. (2016). The uniqueness and greedy method for quadratic compressive sensing. IEEE International Conference on Data Science and Advanced Analytics (DSAA), 808-815.
  12. Li DH, Wang L (2016). A weighted simulation-based estimator for incomplete longitudinal data models. Statistics and Probability Letters, 113, 16-22.
  13. 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.
  14. 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.
liqun.wang@umanitoba.ca    |     Department of Statistics University of Manitoba     |    204 - 474 - 6270