Selected Publications (* denotes student):

• Doubly robust uncertainty quantification for quantile treatment effects in sequential decision making (2025+) with Xu Yang, Chengchun Shi, Shikai Luo and Rui Song. Transactions on Machine Learning Research (TMLR), to appear.

• Offline reinforcement learning for human-guided human-machine interaction with private information. (2025+) With Zeyue Fu, Zhengling Qi, Zhuoran Yang, and Zhaoran Wang, Management Science, to appear.

• Distributional off-policy evaluation in reinforcement learning (2025+) with Zhengling Qi, Chenjia Bai and Zhaoran Wang.  Journal of the American Statistical Association, to appear.

• Tuoyi Zhao*, Wen-Xin Zhou and Lan Wang (2025) Private optimal inventory policy learning for feature-based newsvendor with unknown demand. Management Science, 71 (7), 5419-6318.

• Zeyu Bian*, Chengchun Shi, Zhengling Qi and Lan Wang (2025)  Off-policy evaluation in doubly inhomogeneous environments,  Journal of the American Statistical Association, 120 (550), 1102–1114.

• Guangwei Wen, Charless Doss, Lan Wang, Ira Moscovice and Tongtan Chantarat. (2024) A nonparametric doubly robust test for a continuous treatment effect, Annals of Statistics, 52(4), 1592-1615.

• Lan Wang and Xuming He (2024) Analysis of  Global and Local Optima of Regularized Quantile Regression in High Dimension: A Subgradient Approach,  Econometric Theory, vol 40, 233-277.

• Yu Zhou*, Lan Wang, Rui Song and Tuoyi Zhao* (2023) Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes,  Journal of the American Statistical Association, 118 (544), 2632-2644.

• Ethan X. Fang, Zhaoran Wang and Lan Wang (2023) Fairness-Oriented Learning for Optimal Individualized Treatment Rules, Journal of the American Statistical Association, 118, 1733--1746.

• Wu, Y*,  L. Wang and H. Fu (2023) Model-Assisted Uniformly Honest  Inference for Optimal Treatment Regimes in High Dimension,   Journal of the American Statistical Association, 118: 305–314.

• Tan, K.M., L. Wang and Zhou, W. X. (2022)  High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization, by Journal of the Royal Statistical Society, Series B, 84(1), 205–233.

• Olukunle Owolabi, Kathryn Lawson, Sanhita Sengupta*, Yingsi Huang*, Lan Wang, Chaopeng Shen, Mila Sherman, Deborah Sunter. (2022) A robust statistical analysis of the role of hydropower on the system electricity price and price volatility. Environmental Research Communications,  4(7), 075003.

• Mei-Ling Feng, Olukunle O. Owolabi, Toryn Schafer, Sanhita Sengupta*, Lan Wang, David S. Matteson, Judy P. Che-Castaldo,and Deborah A. Sunter. (2022)  Analysis of animal-related electric outages using species distribution models and community science data.  Environmental Research: Ecology, 1, 011004.

• Yunan Wu* and Lan Wang. (2021)  Resampling-based confidence intervals for model-free robust inference on optimal treatment regimes.  Biometrics, 77(2):465-476.

• Shanshan Ding, Zhihua Su, Guangyu Zhu and Lan Wang. (2021) Envelope quantile regression.  Statistica Sinica,  31, 79-106. Link to web supplement.

• Lan Wang, Bo Peng*, Jelena Bradic, Runze Li and Yunan Wu*. (2020) A tuning-free robust and efficient approach to high-dimensional regression.    Journal of the American Statistical Association, 115, 1700-1714.

(Editors' invited paper with discussions).   

Link to online supplement

Discussions from Professors. Jianqing Fan , Cong Ma & Kaizheng Wang 

Discussions from Professor Po-Ling Loh

Discussion from Professors Xiudi Li & Ali Shojaie 

Authors' rejoinder

Our invited talk slides for JSM 2020

• Shanshan Ding, Wei Qian and Lan Wang. (2020) Double-slicing assisted sufficient dimension reduction for high dimensional censored data.  Annals of Statistics,  48(4) 2132-2154. Link to web supplement

• Yunan Wu* and Lan Wang. (2020) A survey of tuning parameter selection for high-dimensional regression.  Annual Review of Statistics and Its Application, 7: 209-226.

• Lan Wang, Ingrid Van Keilegom and Adam Maidman*. (2018) Wild residual bootstrap inference for penalized quantile regression with heteroscedastic errors.  Biometrika, 105(4),  859--872.  Link to web supplement

• Jianxuan Liu, Yanyuan Ma and Lan Wang. (2018) A new robust estimator of average treatment effect in causal inference. Biometrics, 74, 910--923.  Link to online supplement

• Shuhan Liang, Wenbin Lu, Rui Song and Lan Wang. (2018) Sparse concordance-assisted learning for optimal treatment decision.  Journal of Machine Learning Research, 18(202):1-26.

• Adam Maidman* and Lan Wang. (2018) New semiparametric method for predicting high-cost patients. Biometrics, 74(3):1104--1111. Link to online supplement

• Lan Wang, Yu Zhou*, Rui Song and Ben Sherwood*. (2018) Quantile-optimal treatment regimes.  Journal of the American Statistical Association, 113, 1243-1254.

•  Xu, G.J., Sit, T., Wang, L. and Huang, C-Y. (2017)  Quantile regression under general biased sampling scheme. Journal of the American Statistical Association, 112, 1571-1586. Link to online supplement

• Liqun Yu, Nan Lin and Lan Wang (2017) A parallel algorithm for large-scale nonconvex penalized quantile regression. Journal of Computational and Graphical Statistics, 935-939.

• Gul Inan and Lan Wang (2017) PGEE: An R Package for Analysis of Longitudinal Data with High-Dimensional Covariates The R Journal, 9(1), 393-402.

• Peng, B.*, Wang, L. and Wu, Y. (2016) An Error Bound for  L_1-norm Support Vector Machine Coefficients in Ultra-high Dimension. Journal of Machine Learning Research, 17(236):1-26.

• Jinyuan Chang,Wen Zhou, Wen-Xin Zhou and Lan Wang. (2017) Comparing Large Covariance Matrices under Weak Conditions on the Dependence Structure and its Application to Gene Clustering. Biometrics, 73, 31-41. Link to online supplement

• Lan Wang. (2016+) Nonconvex Penalized Quantile Regression: a Review of Methods, Theory and Algorithms for High-dimensional Heterogeneous Data Analysis. Invited book chapter for Handbook of Quantile Regression, edited by Roger Koenker, Victor Chernozhukov, Xuming He and Limin Peng.

• Hong, H. G., Wang, L. and He, X. (2016) A data-driven approach to conditional screening of high dimensional variables. Stat, 5, 200-212.

• Wang, L. and Sherwood, B*. (2016) Invited discussion on “Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood” by Yang, Wang and He, International Statistical Review, 84(3), 356-359.

• Sherwood, B*. and Wang, L. (2016) Partially linear additive quantile regression in ultra-high dimension. Annals of Statistics, 44, 288-317. Link to online supplement

•  Zhang, X.,  Wu, Y., Wang, L. and Li. R. (2016) A consistent information criterion for support vector machine in diverging model space. Journal of Machine Learning Research, 17(16), 1-26.

• Wang, L., Peng, B*. and Li., R. (2015) A high-dimensional nonparametric multivariate test for mean vector. Journal of the American Statistical Association, 110, 1658-1669.

• Zhang, X., Wu, Y., Wang, L. and Li., R. (2016) Variable selection for support vector machines in moderately high dimensions.  Journal of the Royal Statistical Society, Series B, 78, 53-76.

• Peng, B*. and Wang, L. (2015) An iterative coordinate-descent algorithm for high-dimensional nonconvex penalized quantile regression. Journal of Computational and Graphical Statistics, 24(3), 676-694.

• Wang, L., Sherwood, B*. and Li, R.  (2014) Discussion on “Estimation and Accuracy after Model Selection" by Brad Efron. Journal of the American Statistical Association, 109, 1007-1010.

• Heng, L., Liang, H. and Wang, L. (2014) Generalized additive partial linear models for clustered data with diverging number of covariates using GEE.  Statistica Sinica, 24, 173-196.

• Wey, A., Wang, L. and Rudser, K.  (2014) Censored quantile regression with recursive partitioning based weights.  Biostatistics, 15, 170-181.

• Huixia Wang and Lan Wang. (2014)   Quantile regression analysis of length-biased survival data. Stat, 3, 31-47.

• Wang, L, Yongdai Kim and Runze, Li . (2013)  Calibrating non-convex penalized regression in ultra-high dimension.  Annals of Statistics, 41, 2505-2536.

• Wang, L., Kai, B., Cedric,H. and Tsai, CL. (2013)   Penalized profiled semiparametric estimating functions.  Electronic Journal of Statistics, 7, 2656-2682.

• Sherwood, B*., Wang, L. and Zhou, A. (2013)  Weighted quantile regression for analyzing health care cost data with missing covariates. Statistics in Medicine, 32, 4967-4979.

• He, X., Wang, L. and Hong, H. (2013) Quantile-adaptive model-free nonlinear feature screening for high-dimensional heterogeneous data. Annals of Statistics, 41, 342-369. Link to an example.   Correction of the typo in Example 3 of the paper.

• Luo, X.H., Huang, C.Y. and Wang, L. (2013) Quantile regression for recurrent gap time data.  Biometrics, 69, 375-385. [Web-based supplemental document]

• Lan Wang, Yichao Wu and Runze Li (2012) Quantile regression of analyzing heterogeneity in ultra-high dimension,  Journal of the American Statistical Association, 107, 214-222.

• Lan Wang, Jianhui Zhou and Annie Qu (2012) High-dimensional penalized generalized estimating equations for longitudinal data analysis, Biometrics, 68, 353–360. [Web-based supplemental document]

• Lan Wang (2011) GEE analysis of clustered binary data with diverging number of covariates, Annals of Statistics, 39, 389-417. Here is an online supplemental file that contains additional technical details.  Correction

• Lan Wang (2011) Rank regression under possible model misspecification, Nonparametric Statistics and Mixture Models: A Festschrift in Honor of Thomas P. Hettmansperger (ed by Hunter, Richards and Rosenberger), 317-335.

• Ingrid Van Keilegom and Lan Wang (2010) Semiparametric modeling and estimation of heteroscedasticity in regression analysis of cross-sectional data, Electronic Journal of Statistics, 4, 133-160.

• Lan Wang, Bo Kai and Runze Li (2009) Local rank inference for varying coefficient models,  Journal of the American Statistical Association, 104, 1631-1645.

• Huixia Wang and Lan Wang (2009) Locally weighted censored quantile regression, Journal of the American Statistical Association, 104, 1117-1128.  Here is a remark of the paper.

• Lan Wang and Runze Li (2009) Weighted Wilcoxon-type smoothly clipped absolute deviation method, Biometrics, 65(2), 564-571.  [Web document]

• Lan Wang (2009) Wilcoxon-type generalized Bayesian information criterion, Biometrika, 96(1), 163-173.

•  Lan Wang and Annie Qu (2009) Consistent model selection and data-driven smooth tests for longitudinal data in the estimating equations approach, Journal of the Royal Statistical Society, Series B, 71(1), 177-190.

• Lan Wang (2008) Nonparametric test for checking lack-of-fit of quantile regression model under random censoring,  Canadian Journal of Statistics, 36(2), 321-336.

• Wang, L., Akritas, M. G., and Van Keilegom, I. (2008) An ANOVA-type nonparametric diagnostic test for heteroscedastic regression models, Journal of Nonparametric Statistics, 20(5):365–382.

• Lan Wang and Xiao-Hua Zhou (2007) Assessing the adequacy of variance function in heteroscedastic regression models, Biometrics, 63(4), 1218-1225.

• Lan Wang and Annie Qu (2007) Robust tests in regression models with omnibus alternatives and bounded influence, Journal of the American Statistical Association, 102, 347-358.

• Lan Wang (2007) A simple nonparametric test for diagnosing nonlinearity in Tobit median regression model, Statistics and Probability Letters, 77(10), 1034-1042.

• Lan Wang and Ingrid Van Keilegom (2007) Nonparametric test for the form of parametric regression with time series errors, Statistica Sinica, 17, 369-386.

• Lan Wang and Michael. G. Akritas (2006) Testing for covariate effects in fully nonparametric ANCOVA model,  Journal of the American Statistical Association, 101, 722-736.

• Lan Wang and Michael. G. Akritas (2006) Two-way heteroscedastic ANOVA with large number of levels, Statistica Sinica, 16, 1387-1408.

• Lan Wang and Xiao-Hua Zhou (2005) A fully nonparametric diagnostic test for homogeneity of variances, Canadian Journal of Statistics, 33(4), 545-558.