Department of Statistics
University of Manitoba 		 
   
   
 

Education and Experience

    Liqun Wang received his Bachelor degree in mathematics from Northern Jiaotong University, Master degree in statistics from Beijing Normal University, and Doctoral degree in statistics and econometrics from Vienna University of Technology. He also received a Postgraduate Diploma in mathematical and computer sciences from Vienna Institute for Advanced Studies. He was a post-doctoral research fellow at Universities of Hannover and Dortmund, an assistant professor at University of Basel, and a visiting scholar at University of Southern California, University of California at Berkeley, and University of Toronto.

Research Interests

    Liqun Wang's research areas are applied probability and statistical theory and methodology for complex data analysis. His current research includes boundary crossing probability (first passage time) for diffusion processes, identification and estimation in nonlinear measurement error models and in longitudinal data models, 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 statistical applications in engineering optimization, environmental, medical and health sciences.

Most-Cited Papers by Google Scholar

  1. Wang L, Shan S, Wang GG (2004). Mode-pursuing sampling method for global optimization on expensive black-box functions. Engineering Optimization, 36, 419-438.
  2. Wang L, Poetzelberger K (1997). Boundary crossing probability for Brownian motion and general boundaries. Journal of Applied Probability, 34, 54-65.
  3. Poetzelberger K, Wang L (2001). Boundary crossing probability for Brownian motion. Journal of Applied Probability, 38, 152-164.
  4. Wang L (2004). Estimation of nonlinear models with Berkson measurement errors. Annals of Statistics, 32, 2559-2579.
  5. 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.
  6. Wang L, Poetzelberger K (2007). Crossing probabilities for diffusion processes with piecewise continuous boundaries. Methodology and Computing in Applied Probability, 9, 21-40.
  7. Wang L, Leblanc A (2008). Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 60, 883-900.
  8. Fu JC, Wang L, Lou WY (2003). On exact and large deviation approximation for the distribution of the longest run in a sequence of two-state Markov dependent trials. Journal of Applied Probability, 40, 346-360.
  9. Wang L (2003). Estimation of nonlinear Berkson-type measurement error models. Statistica Sinica, 13, 1201-1210.
  10. 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.
  11. Wang L, Hsiao C (1995). A simulated semiparametric estimation of nonlinear errors-in-variables models. Working Paper, Department of Economics, University of Southern California.
  12. Wang L (1998). Estimation of censored linear errors-in-variables models. Journal of Econometrics, 84, 383-400.
  13. Wang L (1990). Generalized shrunken least squares estimators. Chinese Journal of Applied Probability and Statistics, 6, 225-232.
  14. Wang L, Hsiao C (2011). Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models. Journal of Econometrics, 165, 30-44.
  15. Abarin T, Wang L (2006). Comparison of GMM with second-order least squares estimator in nonlinear models. Far East Journal of Theoretical Statistics, 20, 179-196.
  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.

Some Other Recent Publications

  1. Fan J, Kong L, Wang L, Xiu N. (2017). Variable selection in sparse regression with quadratic measurements. Statistica Sinica, doi:10.5705/ss.202015.0335.
  2. Jin Z, Wang L (2017). First passage time for Brownian motion and piecewise linear boundaries. Methodology and Computing in Applied Probability, 19, 237-253.
  3. 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.
  4. Li DH, Wang L (2016). A weighted simulation-based estimator for incomplete longitudinal data models. Statistics and Probability Letters, 113, 16-22.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Wang L, Lee CH (2014). Discretization-based direct random sample generation. Computational Statistics and Data Analysis, 71, 1001-1010.
  11. Li D, Wang L (2013). A semiparametric estimation approach for linear mixed models. Communications in Statistics - Theory and Methods, 42, 1982-1997.
  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. Li H, Wang L (2012a). A consistent simulation-based estimator in generalized linear mixed models. Journal of Statistical Computation and Simulation, 82, 1085-1103.
  14. Li, H., Wang, L (2012b). Consistent estimation in generalized linear mixed models with measurement error. Journal of Biometrics and Biostatistics, S7:007, doi:10.4172/2155-6180.S7-007.
  15. Chen S, Hsiao C, Wang L (2012). Measurement errors and censored structural latent variables models. Econometric Theory, 28, 696-703.
liqun.wang@umanitoba.ca    |     332 Machray Hall Department of Statistics University of Manitoba     |    204 - 474 - 6270