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 diploma in mathematical and computer sciences. Liqun Wang has research and teaching experience at various universities in Europe and North-America. He has served as an editor, associate editor and editorial board member for a number of statistical journals.

Research Interests

    Liqun Wang's research interests include nonlinear statistical inference, identifiability and estimation in measurement error models, regularized estimation in high-dimensional models, boundary crossing probability and first passage time for diffusion processes, and Monte Carlo simulation methods for statistical computation and optimization. He is also interested in biostatistics and econometrics.

Some Selected Papers by Liqun Wang

  1. 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.
  2. Wang L. (2004). Estimation of nonlinear models with Berkson measurement errors. Annals of Statistics, 32, 2559-2579.
  3. Wang L. (2003). Estimation of nonlinear Berkson-type measurement error models. Statistica Sinica, 13, 1201-1210.
  4. Wang L. (2007). A unified approach to estimation of nonlinear mixed effects and Berkson measurement error models. Canadian Journal of Statistics, 35, 233-248.
  5. Wang L, Hsiao C. (2011). Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models. Journal of Econometrics, 165, 30-44.
  6. Wang L. (1998). Estimation of censored linear errors-in-variables models. Journal of Econometrics, 84, 383-400.
  7. Wang L, Leblanc A. (2008). Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 60, 883-900.
  8. Salamh M, Wang L. (2021). Second-order least squares estimation in nonlinear time series models with ARCH errors. Econometrics, 9(4): 41.
  9. 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.
  10. Wang L, Hsiao C. (2007). Two-stage estimation of limited dependent variable models with errors-in-variables. Econometrics Journal, 10, 426-438.
  11. 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.
  12. Jin Z, Wang L (2017). First passage time for Brownian motion and piecewise linear boundaries. Methodology and Computing in Applied Probability, 19, 237-253.
  13. Wang L, Poetzelberger K. (1997). Boundary crossing probability for Brownian motion and general boundaries. Journal of Applied Probability, 34, 54-65.
  14. Poetzelberger K, Wang L. (2001). Boundary crossing probability for Brownian motion. Journal of Applied Probability, 38, 152-164.
  15. Wang L, Poetzelberger K. (2007). Crossing probabilities for diffusion processes with piecewise continuous boundaries. Methodology and Computing in Applied Probability, 9, 21-40.
  16. 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.
  17. Wang L, Lee CH. (2014). Discretization-based direct random sample generation. Computational Statistics and Data Analysis, 71, 1001-1010.
  18. Wang L, Shan S, Wang GG. (2004). Mode-pursuing sampling method for global optimization on expensive black-box functions. Engineering Optimization, 36, 419-438.
  19. 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. Li M, Ma Y, Wang L. (2026). Quantile regression with measurement errors. Electronic Journal of Statistics, 20 (2), 2943 - 2963.
  2. Wang X, Zhao H, Zhou Z, Kong L, Wang L. (2026). Adaptive 𝓁𝑞 regularized estimation for high-dimensional sparse covariance matrix. Journal of Multivariate Analysis.
  3. Yang Y, Wang L, Wang L. (2025). Bayesian shrinkage inference for seemingly unrelated regression models. Journal of the Korean Statistical Society.
  4. Jiang J, Wang L, Wang L. (2025). Bayesian analysis of censored measurement error models using finite mixture of heavy-tailed distributions. AStA Advances in Statistical Analysis.
  5. Afzali E, Muthukumarana S, Wang L. (2024). Navigating interpretability and alpha control in GF-KCSD testing with measurement error: A Kernel approach. Machine Learning with Applications.
  6. Xue L, Wang L. (2024). Instrumental variable method for regularized estimation in generalized linear measurement error models. Econometrics, 12:21.
  7. Wang X, Kong L, Wang L. (2024). Estimation of sparse covariance matrix via non-convex regularization. Journal of Multivariate Analysis.
  8. Song Q, Wang L, Wang L. (2023). Two-stage shrunken least squares estimator and its superiority. Communications in Statistics-Theory and Methods.
  9. Wang X, Kong L, Zhuang X, Wang L. (2023). Variance estimation in high-dimensional linear regression via adaptive elastic-net. Journal of Industrial and Management Optimization.
  10. Wang Q, Wang L, Wang L. (2023). Bayesian instrumental variable estimation in linear meaurement error models. Canadian Journal of Statistics.
  11. Wang X, Kong L, Wang L. (2023). Numerical optimization and computation for second-order least squares estimation. Pacific Journal of Optimization, 19, 315-334.
  12. Wang X, Kong L, Wang L, Yang Z. (2023). High-dimensional covariance estimation via constrained Lq-type regularization. Mathematics, 11(4): 1022.
  13. 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.
  14. 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.
  15. Wang X, Kong L, Wang L. (2022). Estimation of error variance in regularized regression models via adaptive lasso. Mathematics, 10(11): 1937.
  16. 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.
  17. Wang L. (2021). Estimation in mixed-efects models with measurement error. In G.Y. Yi, A. Delaigle, P. Gustafson (eds), Handbook of Measurement Error Models, pp. 359-377, Chapman & Hall/CRC.
  18. 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.
liqun.wang@umanitoba.ca    |     Department of Statistics University of Manitoba     |    204 - 474 - 6270