High-dimensional statistics, machine learning, causal inference, compositional data analysis, survival analysis, statistical genetics and genomics
Representative scientific research projects
General Program of the National Natural Science Foundation of China Grant 11671018, “Sparse and Low-Rank Modeling and Inference of High-Dimensional Complex Data,” PI, 1/2017-12/2020, 480,000
National Key R&D Program of China Grant 2016YFC0207703, “Construction of Multi-Pollutant Data Fields Based on Statistical and Numerical Models,” PI, 7/2016–6/2020, 3,173,000
10 representative papers
1. Zhang, J. and Lin, W. (2019). Scalable estimation and regularization for the logistic normal multinomial model. Biometrics, to appear.
2. Cao, Y., Lin, W. and Li, H. (2019). Large covariance estimation for compositional data via composition-adjusted thresholding. Journal of the American Statistical Association, to appear.
3. Cao, Y., Lin, W. and Li, H. (2018). Two-sample tests of high-dimensional means for compositional data. Biometrika, 105, 115-132.
4. Lin, W., Feng, R. and Li, H. (2015). Regularization methods for high-dimensional instrumental variables regression with an application to genetical genomics. Journal of the American Statistical Association, 110, 270-288.
5. Lin, W., Shi, P., Feng, R. and Li, H. (2014). Variable selection in regression with compositional covariates. Biometrika, 101, 785-797.
6. Lin, W. and Lv, J. (2013). High-dimensional sparse additive hazards regression. Journal of the American Statistical Association, 108, 247-264.
