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 is an elected member of the International Statistical Institute (ISI).
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Liqun Wang's research interests include boundary crossing problem for Brownian motion and diffusion processes, estimation in nonlinear models with measurement error, 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 engineering design and optimizaiton.
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- Wang L (2004). Estimation of nonlinear models with Berkson measurement errors. Annals of Statistics, 32, 2559-2579.
- Wang L (2003). Estimation of nonlinear Berkson-type measurement error models. Statistica Sinica, 13, 1201-1210.
- Wang L (2007). A unified approach to estimation of nonlinear mixed effects and Berkson measurement error models. Canadian Journal of Statistics, 35, 233-248.
- Wang L, Hsiao C (2011). Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models. Journal of Econometrics, 165, 30-44.
- 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.
- Wang L, Leblanc A (2008). Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 60, 883-900.
- Wang L (1998). Estimation of censored linear errors-in-variables models. Journal of Econometrics, 84, 383-400.
- Wang L, Hsiao C (1995). A simulated semiparametric estimation of nonlinear errors-in-variables models. Working Paper, Department of Economics, University of Southern California.
- Wang L (1990). Generalized shrunken least squares estimators. Chinese Journal of Applied Probability and Statistics, 6, 225-232.
- Wang L, Poetzelberger K (1997). Boundary crossing probability for Brownian motion and general boundaries. Journal of Applied Probability, 34, 54-65.
- Poetzelberger K, Wang L (2001). Boundary crossing probability for Brownian motion. Journal of Applied Probability, 38, 152-164.
- Wang L, Poetzelberger K (2007). Crossing probabilities for diffusion processes with piecewise continuous boundaries. Methodology and Computing in Applied Probability, 9, 21-40.
- 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.
- Wang L, Shan S, Wang GG (2004). Mode-pursuing sampling method for global optimization on expensive black-box functions. Engineering Optimization, 36, 419-438.
- 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.
- 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.
- 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.
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- Fan J, Wang L, Yan A. (2019). An Inexact Projected Gradient Method for Sparsity-Constrained Quadratic Measurements Regression. Asia-Pacific Journal of Operational Research, 36, 1940008.
- 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.
- Zhu, Q. et al. (2018).
Identifying an early treatment window for predicting breast cancer response to neoadjuvant chemotherapy using immunohistopathology and hemoglobin parameters., Breast Cancer Research, 20:56.
- Fan J, Kong L, Wang L, Xiu N. (2018). Variable selection in sparse regression with quadratic measurements. Statistica Sinica, 28, 1157-1178.
- 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.
- Jin Z, Wang L (2017). First passage time for Brownian motion and piecewise linear boundaries. Methodology and Computing in Applied Probability, 19, 237-253.
- 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.
- Li DH, Wang L (2016). A weighted simulation-based estimator for incomplete longitudinal data models. Statistics and Probability Letters, 113, 16-22.
- 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.
- 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.
- 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.
- 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.
- Wang L, Lee CH (2014). Discretization-based direct random sample generation. Computational Statistics and Data Analysis, 71, 1001-1010.
- Li D, Wang L (2013). A semiparametric estimation approach for linear mixed models. Communications in Statistics - Theory and Methods, 42, 1982-1997.
- 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.
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