Research interests:

Nonparametric and semiparametric statistics with focus on high-dimensional data analysis, quantile regression, data-driven decision making, survival analysis.

Editorial service:

Associate Editor of Journal of the Royal Statistical Society, Series B (04/2013-08/2016)

Associate Editor of Biometrics (07/2011-present)

Associate Editor of Journal of the American Statistical Association (07/2012-present)

Associate Editor of Annals of Statistics (01/2016-present)

Co-editor, special issue of Econometrics and Statistics on Quantile Regression and Semiparametric Methods, 2017.

Selected Papers (* denotes student):

(Acknowledgement: The research of Dr. Wang has been supported by NSF since 2007.)

• Yunan Wu* and Lan Wang. (2019+) A survey of tuning parameter selection for high-dimensional regression. To appear in Annual Review of Statistics and Its Application.
• Shanshan Ding, Wei Qian and Lan Wang. (2019+) Double-slicing assisted sufficient dimension reduction for high dimensional censored data. To appear in Annals of Statistics.

Link to supplement

• Shanshan Ding, Zhihua Su, Guangyu Zhu and Lan Wang. (2019+) Envelope quantile regression. To appear in Statistica Sinica.
• 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 online 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.
• Lan Wang, Yu Zhou*, Rui Song and Ben Sherwood*. (2018) Quantile-optimal treatment regimes. Journal of the American Statistical Association, 113, 1243--1254.

R package: quantoptr

· 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.

• 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.
• 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.
• Quantile regression of analyzing heterogeneity in ultra-high dimension (2012), by Lan Wang, Yichao Wu and Runze Li,

Journal of the American Statistical Association, 107, 214-222.

• High-dimensional penalized generalized estimating equations for longitudinal data analysis (2012), by Lan Wang, Jianhui Zhou and Annie Qu, Biometrics, 68, 353–360. [Web-based supplemental document]
• GEE analysis of clustered binary data with diverging number of covariates (2011), by Lan Wang, Annals of Statistics, 39, 389-417.

Here is an online supplemental file that contains additional technical details.

• Rank regression under possible model misspecification (2011), by Lan Wang, Nonparametric Statistics and Mixture Models: A Festschrift in Honor of Thomas P. Hettmansperger (ed by Hunter, Richards and Rosenberger), 317-335.
• Semiparametric modeling and estimation of heteroscedasticity in regression analysis of cross-sectional data (2010), by Ingrid Van Keilegom and Lan Wang, Electronic Journal of Statistics, 4, 133-160.
• Local rank inference for varying coefficient models (2009), by Lan Wang, Bo Kai and Runze Li, Journal of the American Statistical Association, 104, 1631-1645.
• Locally weighted censored quantile regression (2009), by Huixia Wang and Lan Wang, Journal of the American Statistical Association, 104, 1117-1128.  Here is a remark of the paper.
• Weighted Wilcoxon-type smoothly clipped absolute deviation method (2009), by Lan Wang and Runze Li, Biometrics, 65(2), 564-571.  [Web document]
• Wilcoxon-type generalized Bayesian information criterion (2009), by Lan Wang, Biometrika, 96(1), 163-173.
• Consistent model selection and data-driven smooth tests for longitudinal data in the estimating equations approach (2009), by Lan Wang and Annie Qu, Journal of the Royal Statistical Society, Series B, 71(1), 177-190.
• Nonparametric test for checking lack-of-fit of quantile regression model under random censoring (2008), by Lan Wang, Canadian Journal of Statistics, 36(2), 321-336.
• An ANOVA-type nonparametric diagnostic test for heteroscedastic regression models (2008), by Wang, L., Akritas, M. G., and Van Keilegom, I., Journal of Nonparametric Statistics, 20(5):365–382.
• Assessing the adequacy of variance function in heteroscedastic regression models (2007), by Lan Wang and Xiao-Hua Zhou, Biometrics, 63(4), 1218-1225.
• Robust tests in regression models with omnibus alternatives and bounded influence (2007), by Lan Wang and Annie Qu, Journal of the American Statistical Association, 102, 347-358.
• A simple nonparametric test for diagnosing nonlinearity in Tobit median regression model (2007), by Lan Wang, Statistics and Probability Letters, 77(10), 1034-1042.
• Nonparametric test for the form of parametric regression with time series errors (2007), by Lan Wang and Ingrid Van Keilegom, Statistica Sinica, 17, 369-386.
• Testing for covariate effects in fully nonparametric ANCOVA model (2006), by Lan Wang and Michael. G. Akritas,  Journal of the American Statistical Association, 101, 722-736.
• Two-way heteroscedastic ANOVA with large number of levels (2006), by Lan Wang and Michael. G. Akritas, Statistica Sinica, 16, 1387-1408.
• A fully nonparametric diagnostic test for homogeneity of variances (2005), by Lan Wang and Xiao-Hua Zhou, Canadian Journal of Statistics, Vol. 33, No 4, pp. 545-558.