Education and Experience

Liqun Wang obtained Bachelor's and Master's degrees in mathematics and statistics in China, a PhD in statistics and econometrics at the Vienna University of Technology, Austria, and a Postgraduate Diploma in Mathematical and Computer Sciences at the Institute for Advanced Studies in Vienna. 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 stochastic processes. His current research areas include identification and estimation in nonlinear systems, measurement error (errors in variables) problem in regression models, boundary crossing probabilities (first passage time) of diffusion processes, Monte Carlo simulation methods in statistical computation and optimization, and high-dimensional data assimilation. He is also interested in biostatistics, econometrics, as well as statistical applications in engineering optimization, environmental, as well as 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. 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.
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. 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.
8. Wang L, Hsiao C (1995). A simulated semiparametric estimation of nonlinear errors-in-variables models. Working Paper, Department of Economics, University of Southern California.
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, Leblanc A (2008). Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 60, 883-900.
11. Wang L, Poetzelberger K (2007). Crossing probabilities for diffusion processes with piecewise continuous boundaries. Methodology and Computing in Applied Probability, 9, 21-40.
12. Wang L (1990). Generalized shrunken least squares estimators. Chinese Journal of Applied Probability and Statistics, 6, 225-232.
13. Wang L (1998). Estimation of censored linear errors-in-variables models. Journal of Econometrics, 84, 383-400.
14. 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.

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.