Yuanyuan Lin (林媛媛)

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Department of Statistics
The Chinese University of Hong Kong
Lady Shaw Building 113, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Email: ylin“at”sta“dot”cuhk“dot”edu“dot”hk
Phone: +852 3943-7924

About Me

I am now an Associate Professor in the Department of Statistics at the Chinese University of Hong Kong. My current research interests include statistical machine learning, high dimensional statistics, semiparametric and nonparametric inference, etc.

Selected Publications

  1. Shen G., Jiao, Y., Lin, Y., Horowitz J. L. and Huang, J. (2024). Nonparametric estimation of Non-crossing quantile regression process with deep ReQU neural networks. Journal of Machine Learning Research, in press.

  2. Song, S., Lin, Y. and Zhou, Y. (2024). Semi-supervised inference for block-wise missing data without imputation. Journal of Machine Learning Research, in press.

  3. Chun-Kwan O, Brian Wai-Hei Siu, Vanessa Wai-Shan Leung, Yuan-yuan Lin, Chen-zhao Ding, Eric Siu-Him Lau, Andrea On-Yan Luk, Elaine Yee-Kwan Chow, Ronald Ching-Wan Ma, Juliana Chung-Ngor Chan, Rachel Ngan-Yin Chan, Yun Kwok Wing, Alice Pik-Shan Kong (2023+). Associations of insomnia, glycemic control and non-insulin use with incident chronic cognitive impairment in older adults with type 2 diabetes: a prospective study of the Hong Kong Diabetes Register. Journal of Diabetes and its Complications, in press.

  4. Han, M., Lin, Y., Liu, W. and Wang, Z. (2023+). Robust inference for subgroup analysis with general transformation model. Journal of Statistical Planning and Inference, in press.

  5. Han, R., Luo, L., Lin, Y. and Huang, J. (2024). Online inference with debiased stochastic gradient descent. Biometrika 111 91-108.

  6. Luo, L., Han, R., Lin, Y. and Huang, J. (2023). Online inference in high-dimensional generalized linear models with streaming data. Electronic Journal of Statistics 17, 3443-3471.

  7. Wang, T., Tang, W., Lin, Y. and Su, W. (2023). Semi-supervised inference for nonparametric logistic regression. Statistics in Medicine 42, 2573-2589.

  8. Han, D., Han, M., Huang, J. and Lin, Y. (2023). Robust signal recovery for high-dimensional single index models. Scandinavian Journal of Statistics 50, 1590-1615.

  9. Jiao, Y., Shen G., Lin, Y. and Huang, J. (2023). Deep nonparametric regression on approximate manifolds: Nonasymptotic error bounds with polynomial prefactors. Annals of Statistics 51, 691-716.

  10. Song, S., Lin, Y. and Zhou, Y. (2023). A general M-estimation theory in semi-supervised framework. Journal of the American Statistical Association.

  11. Shen G., Jiao, Y., Lin, Y. and Huang, J. (2022). Approximation with CNNs in Sobolev Space: with Applications to Classification. Advances in Neural Information Processing Systems 25, 2876-2888. (NeurIPS 2022,“ Oral ” paper.)

  12. Hao, M., Lin, Y., Shen, G. and Su, W. (2022). Nonparametric inference on smoothed quantile regression process. Comp. Stat. & Data Anal. 179, 107645.

  13. Shen G., Chen, K., Huang J. and Lin, Y. (2023). Linearized maximum rank correlation estimation. Biometrika 110, 187-203.

  14. Lin, Y., Xie, J., Han, R. and Tang, N. (2023). Post-selection inference for high-dimensional logistic regression under case-control design. Journal of Business and Economic Statistics 41, 624-635.

  15. Han, D., Huang, J., Lin, Y., Liu, L., Qu, L. and Sun, L. Q. (2023). Robust signal recovery for high-dimensional log-contrast models with compositional covariates. Journal of Business and Economic Statistics 41, 957-967.

  16. Hao, M., Lin, Y., Liu, K. and Zhao, X. (2022). Penalized nonparametric likelihood-based inference for current status data model. Electronic Journal of Statistics 16, 3099--3134.

  17. Song, S., Lin, Y. and Zhou, Y. (2021). Linear expectile regression under massive data. Fundamental Research 1, 574-585.

  18. Han, D., Huang, J., Lin, Y. and Shen, G. (2022). Robust post-selection inference of high dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors. Journal of Econometrics 230, 416--431.

  19. Dai, L., Chen, K., Li, G. and Lin, Y. (2022). Metric learning via cross-validation. Statistica Sinica 32, 1701-1721.

  20. Tang, W., Xie, J., Lin, Y. and Tang, N. (2021). Quantile correlation-based variable selection. Journal of Business and Economic Statistics 40, 1081-1093.

  21. 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.

  22. Wang, Z., Liu, X., Tang, W. and Lin, Y. (2021). Incorporating graphical structure of predictors in sparse quantile regression. Journal of Business and Economic Statistics 39, 783-792.

  23. 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.

  24. Zhou J., Shen, G., Chen, X. and Lin, Y. (2020). Efficient fused learning for distributed imbalanced data. Communications in Statistics: Theory and Methods, In press.

  25. Hao, M., Lin, Y. and Zhao, X. (2020). Nonparametric inference of right censored data with smoothing splines. Statistica Sinica 30, 153-173.

  26. Xie, J., Hao, M., Liu W. and Lin, Y. (2020). Fused variable screening with massive imbalanced data. Comp. Stat. & Data Anal. 141, 94-108.

  27. Hao, M., Lin, Y., Liu, X. and Tang, W. (2018). Robust feature screening of ultra-high dimensional survival data. Journal of Applied Statistics 46, 979-994.

  28. 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)

  29. Lin, Y., Luo, Y., Xie, S. and Chen, K. (2017). Robust estimation for general transformation models with random effects. Biometrika 104, 971-986.

  30. 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.

  31. Chen, K., Lin, Y., Yao, Y. and Zhou, C. (2017). Regression analysis with response-biased sampling. Statistica Sinica 27, 1699-1714.

  32. Hao, M., Lin, Y. and Zhao, X. (2016). A relative error-based approach for variable selection. Comp. Stat. & Data Anal. 103, 250-262.

  33. Liu X., Lin, Y. and Wang, Z. (2016). Group variable selection for relative error regression. Journal of Statistical Planning and Inference 175, 40-50.

  34. Chen, K., Lin, Y., Wang, Z. and Ying, Z. (2016). Least product relative error regression. Journal of Multivariate Analysis 144, 91-98.

  35. Wang, Z., Liu, W. and Lin, Y. (2015). A change-point problem in relative error-based regression. Test 24, 835-856.

  36. Li, Z., Lin, Y., Zhou, G. and Zhou, W. (2014) Empirical likelihood for least absolute relative error regression. Test 23, 86-99.

  37. Lin, Y. and Chen, K. (2013). Efficient estimation of the censored linear regression model. Biometrika 100, 525-530.

  38. Chen, K., Guo, S., Lin, Y. and Ying, Z. (2010). Least absolute relative error estimation. Journal of the American Statistical Association 105, 1104-1112. (JASA Featured Article)

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