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, identifiability and estimation in nonlinear models with measurement error, regularized estimation in high-dimensional models 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. Jin Z, Wang L (2017). First passage time for Brownian motion and piecewise linear boundaries. Methodology and Computing in Applied Probability, 19, 237-253.
  5. Wang L. (2004). Estimation of nonlinear models with Berkson measurement errors. Annals of Statistics, 32, 2559-2579.
  6. Wang L. (2003). Estimation of nonlinear Berkson-type measurement error models. Statistica Sinica, 13, 1201-1210.
  7. Wang L. (2007). A unified approach to estimation of nonlinear mixed effects and Berkson measurement error models. Canadian Journal of Statistics, 35, 233-248.
  8. Wang L, Leblanc A. (2008). Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 60, 883-900.
  9. Wang L, Hsiao C. (2011). Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models. Journal of Econometrics, 165, 30-44.
  10. Wang L, Hsiao C. (2007). Two-stage estimation of limited dependent variable models with errors-in-variables. Econometrics Journal, 10, 426-438.
  11. Wang L. (1998). Estimation of censored linear errors-in-variables models. Journal of Econometrics, 84, 383-400.
  12. 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.
  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. 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.
  15. Wang L, Lee CH. (2014). Discretization-based direct random sample generation. Computational Statistics and Data Analysis, 71, 1001-1010.
  16. Wang L, Shan S, Wang GG. (2004). Mode-pursuing sampling method for global optimization on expensive black-box functions. Engineering Optimization, 36, 419-438.
  17. 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. Wang Q, Wang L, Wang L. (2023). Bayesian instrumental variable estimation in linear meaurement error models. Canadian Journal of Statistics.
  2. Wang X, Kong L, Wang L. (2023). Numerical optimization and computation for second-order least squares estimation. Pacific Journal of Optimization, 19, 315-334.
  3. Wang X, Kong L, Wang L, Yang Z. (2023). High-dimensional covariance estimation via constrained Lq-type regularization. Mathematics, 11(4): 1022.
  4. Jiang J, Wang L, Wang L. (2022). Approximate Bayesian estimator for the parameter vector in linear models with multivariate t distribution errors. Communications in Statistics-Theory and Methods.
  5. Salamh M, Wang L. (2022). Identifiability and estimation of autoregressive ARCH models with measurement error. In W. He, L. Wang, J. Chen, C.D. Lin (eds), Advances and Innovations in Statistics and Data Sciences, pp. 235-255, Springer.
  6. Wang X, Kong L, Wang L. (2022). Estimation of error variance in regularized regression models via adaptive lasso. Mathematics, 10(11): 1937.
  7. Yanaç K, Adegoke A, Wang L, Uyaguari M, Yuan Q. (2022). Detection of SARS-CoV-2 RNA throughout wastewater treatment plants and a modeling approach to understand COVID-19 infection dynamics in Winnipeg, Canada, Science of the Total Environment, 825: 153906.
  8. Jiang J, Wang L, Wang L. (2022). Linear approximate Bayes estimator for regression parameter with an inequality constraint. Communications in Statistics - Theory and Methods, 51, 1531-1548.
  9. Salamh M, Wang L. (2021). Second-order least squares estimation in nonlinear time series models with ARCH errors. Econometrics, 9(4): 41.
  10. Salamh M, Wang L. (2021). Second-order least squares method for dynamic panel data models with application. Journal of Risk and Financial Management, 14(9): 410.
  11. Wang L. (2021). Identifiability in measurement error models. In G.Y. Yi, A. Delaigle, P. Gustafson (eds), Handbook of Measurement Error Models, pp. 55-70, Chapman & Hall/CRC.
  12. Wang L. (2021). Estimation in mixed-effects models with measurement error. In G. Y. Yi, A. Delaigle, P. Gustafson (eds), Handbook of Measurement Error Models, pp. 359-377, Chapman & Hall/CRC.
  13. 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, 36, 5164-5174.
  14. 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.
  15. Fan J, Kong L, Wang L, Xiu N. (2018). Variable selection in sparse regression with quadratic measurements. Statistica Sinica, 28, 1157-1178.
liqun.wang@umanitoba.ca    |     Department of Statistics University of Manitoba     |    204 - 474 - 6270