Department of Statistics
University of Manitoba
 
   
   
 

Education and Experience

    Liqun Wang obtained Bachelor's and Master's degrees in mathematics and statistics in China, and a Doctoral degree in applied mathematics (statistics and econometrics) at the Vienna University of Technology, Austria. He has been a research associate at University of Hannover and University of Dortmund, Germany, an assistant professor at University of Basel, Switzerland, a visiting scholar at University of Southern California - Los Angeles, University of California - Berkeley, and University of Toronto. Currently, he is a professor of statistics at the University of Manitoba, Canada.

Research Interests

    Liqun Wang's main research interests are theory and methodology in statistics and applied probability. His current research areas are semiparametric inference in nonlinear models with errors in variables, boundary crossing probabilities (first passage time) of diffusion processes, and Monte Carlo methods in statistical computation and simulation. He is also interested in biostatistics, econometrics, as well as statistical applications in engineering optimization, environmental 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 (2003). Estimation of nonlinear Berkson-type measurement error models. Statistica Sinica, 13, 1201-1210.
  7. Wang L, Hsiao C (1995). A simulated semiparametric estimation of nonlinear errors-in-variables models. Working Paper, Department of Economics, University of Southern California.
  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 GG, Wang L, Shan S (2005). Reliability assessment using discriminative sampling and metamodeling. SAE Technical Paper 2005-01-0349, doi:10.4271/2005-01-0349.
  10. Wang L, Poetzelberger K (2007). Crossing probabilities for diffusion processes with piecewise continuous boundaries. Methodology and Computing in Applied Probability, 9, 21-40.
  11. Wang L, Leblanc A (2008). Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 60, 883-900.
  12. 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.
  13. Wang L (1990). Generalized shrunken least squares estimators. Chinese Journal of Applied Probability and Statistics, 6, 225-232.
  14. Wang L (1998). Estimation of censored linear errors-in-variables models. Journal of Econometrics, 84, 383-400.

Some Other Recent Publications

  1. 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.
  2. Xu K, Ma Y, Wang L (2014). Instrument assisted regression for errors in variables models with binary response. Scandinavian Journal of Statistics, doi: 10.1111/sjos.12097.
  3. 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.
  4. 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.
  5. Wang L, Lee CH (2014). Discretization-based direct random sample generation. Computational Statistics and Data Analysis, 71, 1001-1010.
  6. 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.
  7. Li D, Wang L (2013). A semiparametric estimation approach for linear mixed models. Communications in Statistics - Theory and Methods, 42, 1982-1997.
  8. 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.
  9. Li H, Wang L (2012). A consistent simulation-based estimator in generalized linear mixed models. Journal of Statistical Computation and Simulation, 82, 1085-1103.
  10. 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.
  11. Chen S, Hsiao C, Wang L (2012). Measurement errors and censored structural latent variables models. Econometric Theory, 28, 696-703.
  12. Wang L, Hsiao C (2011). Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models. Journal of Econometrics, 165, 30-44.
  13. Abarin T, Wang L (2009). Second-order least squares estimation of censored regression models. Journal of Statistical Planning and Inference, 139, 125-135.
liqun.wang@umanitoba.ca    |     332 Machray Hall Department of Statistics University of Manitoba     |    204 - 474 - 6270