Academic Background

Research Interest

Selected Publications

• Shen G., Jiao, Y., Lin, Y. and Huang, J. (2022). Approximation with CNNs in Sobolev Space: with Applications to Classification. The Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022). Accepted.
• Shen G., Chen, K., Huang J. and Lin, Y. (2022). Linearized maximum rank correlation estimation. Biometrika. In press.
• Han, D., Huang, J., Lin, Y., Liu, L., Qu, L. and Sun, L. Q. (2022). Robust signal recovery for high-dimensional log-contrast models with compositional covariates. Journal of Business and Economic Statistics. In press.
• Lin, Y., Xie, J., Han, R. and Tang, N. (2022). Post-selection inference for high-dimensional logistic regression under case-control design. Journal of Business and Economic Statistics. In press.
• Hao, M., Lin, Y., Liu, K. and Zhao, X. (2022). Penalized nonparametric likelihood-based inference for current status data model. Electronic Journal of Statistics. In press.
• Song, S., Lin, Y. and Zhou, Y. (2021). Linear expectile regression under massive data. Fundamental Research, 1, 574-585.
• Han, D., Huang, J., Lin, Y. and Shen, G. (2021). Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors. Journal of Econometrics. In press.
• Dai, L., Chen, K., Li, G. and Lin, Y. (2022). Metric learning via cross-validation. Statistica Sinica. 32, 1701-1721.
• Tang, W., Xie, J., Lin, Y. and Tang, N. (2021). Quantile correlation-based variable selection. Journal of Business and Economics Statistics. 40, 1081-1093.
• Wang, Z., Liu, X., Tang, W. and Lin, Y. (2021). Incorporating graphical structure of predictors in sparse quantile regression. Journal of Business and Economics Statistics 39, 783-792.
• Xie, J., Lin, Y., Yan X. and Tang, N. (2020). Category-adaptive variable screening for ultra-high dimensional heterogeneous categorical data. Journal of the American Statistical Association 115, 747-760.
• Chan H. M., Tang, W., Curl P., Lin, Y., Wan S. M. and Ho N. M. (2020). Doping control analysis of total arsenic in equine plasma. Drug Testing and Analysis 12, 1462-1469.
• Hao, M., Lin, Y. and Zhao, X. (2020). Nonparametric inference of right censored data with smoothing splines. Statistica Sinica 30, 153-173.
• Lin, Y., Liu, X. and Hao, M (2018). Model-free feature screening of high dimensional survival data. Science China Mathematics 61, 1617-1636. (Best Paper Award, Science China Mathematics, 2021).
• Lin, Y., Luo, Y., Xie, S. and Chen, K. (2017). Robust estimation for general transformation models with random effects. Biometrika 104, 971-986.
• Wong K. Y., Kwok W. H., Chan, H. M., Choi L. S., Ho N. M., Jaubert M., Bailly-Chouriberry L., Bonnaire Y., Cawley A., Williams H. M., Keledjian J., Brooks L., Chambers A., Lin, Y. and Wan S. M. (2017). Doping control study of AICAR in post-race urine and plasma samples from horses. Drug Testing and Analysis 9, 1363-1371.
• Chen, K., Lin, Y., Yao, Y. and Zhou, C. (2017). Regression analysis with response-selective sampling. Statistica Sinica 27, 1699-1714.
• Chen, K., Lin, Y., Wang, Z. and Ying, Z. (2016). Least product relative error regression. Journal of Multivariate Analysis 144, 91-98.
• Lin, Y. and Chen, K. (2013). Efficient estimation of the censored linear regression model. Biometrika 100, 525-530.
• Chen, K., Guo, S., Lin, Y. and Ying, Z. (2010). Least absolute relative error estimation. Journal of the American Statistical Association 105, 1104-1112.

Honors and Awards

• Best Paper Award, Science China Mathematics (2021)
• Vice-Chancellor’s Exemplary Teaching Award, The Chinese University of Hong Kong (2016)
• Faculty Exemplary Teaching Award, The Chinese University of Hong Kong (2016)

Major Research Grants

Professional Activities