Research
Projects:
2023.1-2026.12 National Natural Science Foundation (NNSF), China, Homogeneity pursuit via mixed integer programming (No. 12271441), Principal Investigator.
2020.1-2022.12 National Natural Science Foundation (NNSF) for the Youth, China, Multilevel semiparametric learning of high dimensional mediation(No. 11901470), Principal Investigator.
2024.12-2029.11, National Key Research and Development Program of China, Young Scientist Project (No.2024YFA1015800), Co-Investigator.
2022.12-2027.11, National key research and development in Mathematics and Applied Research of the Ministry of Science and Technology, Statistical theory and method of distributed learnig, (No.2022YFA1003702), Co-Investigator.
2025.01-2026.12, National Natural Science Foundation (NNSF) for the Youth, China, Distributed and Incremental Multimodal Learning for Long-Tailed Steel Surface Defect Detection, (No.12426309), Co-Investigator.
2020.1-2024.12 the Key of NNSF, China, Inference on semiparametric integration(No.11931014), Co-Investigator.
10/2015-9/2018，NIH/EPA P01 Children's Environmental Health Center(CEHC) at University of Michigan，"To develop statistical methodologies highly relevant to analytic challenges arising in the analysis of environmental exposures in relation to children's growth and maturation", Co-Investigator.
Publications:
Google Scholar: https://scholar.google.com/citations?hl=zh-CN&user=5wpl_sUAAAAJ
46. Zhang, C.#, Zhou, L.#, Guo, B., Lin, H.*(2025). Spatial effect detection regression for large scale spatio-temporal covariates. Journal of the Royal Statistical Society: Series B (Statistical Methodology).87.872-890.
45. Wang, X.#, Zhou, L.#, Lin, H.*(2025). Deep regression learning with optimal loss function. Journal of the American Statistical Association.120:550,1305-1317.
44. Zheng, S. and Zhou, L.*(2025). Inference for High-Dimensional Streamed Longitudinal Data. Acta Mathematica Sinica, English Series.41 (2), 757-779.
43. Zhao, X., Tang, L., Zhang, W., Zhou, L.*(2025). Subgroup learning for multiple mixed-type outcomes with block-structured covariates. Computational Statistics Data Analysis.204, 108105.
42. Liu, W., Li, G.,Zhou, L.*and Luo, L.(2025). High-dimensional large-scale mixed-type data imputation under missing at random. Science China Mathematics,68,4:969-1000.
41. Zhou, L. and PXK Song* (2024). Comments on: Data integration via analysis of subspaces (DIVAS), TEST. 33 (3), 689-692.
40. Li, G.#, You, M.#, Zhou, L., Liang, H. and Lin, H.* (2024). A projection-based diagnostic test for generalized functional regression models. Statistica Sinica ,34 (4), 1973-1995.
39. Zhao, X., Zhou, L., Zhang, W.J. and Lin, H.*(2025). Tensor Decomposition-assisted Multiview Subgroup Analysis. Acta. Math. Sin.-English Ser. 41 (2), 588-618.
38. Xiang, P., Zhou, L. and Tang, L. (2024). Transfer learning via random forests: A one-shot federated approach. Computational Statistics and Data Analysis. 197, 107975.
37. Wang, W., Wu, S., Zhu, Z., Zhou, L. and Song, P. X.-K.(2024). Supervised homogeneity fusion: a combinatorial approach. The Annals of Statistics. 52, 1, 285–310.
36. Zhou, L., Gong, Z., and Xiang, P. (2024). Distributed computing and inference for big data. Annual Review of Statistics and Its Application. 11:533–551.
35. Hector, E., Tang, L., Zhou, L. and Song, P. X.-K. (2024). Data Integration and Model Fusion in the Bayesian and Frequentist Frameworks. Handbook of Bayesian, Fiducial, and Frequentist Inference. 238–263.
34. Liu, W., Luo, L. and Zhou, L.(2023). Online missing value imputation for high-dimensional mixed-type data via generalized factor models. Computational Statistics and Data Analysis, 187, 107822.
33. Song, P.X.-K. and Zhou, L. (2023). Discussion of “Statistical inference for streamed longitudinal data”. Biometrika. 110(4), 859–862.
32. He, Y.#, Zhou, L.#, Xia, Y. and Lin, H.*(2023). Center-augmented l2-type regularization for subgroup learning. Biometrics. 27, 2157–2170.
31. He., Y., Ma, H., Sun, N., Zeng, S., Zhang, Y., Shu, Y., Hua, W., Zhou, T., Zhou, L.and Li, X. (2023). Prognosis of Patients with Hypertrophic Obstructive Cardiomyopathy: A Multicenter Cohort Study with Data-Driven Propensity Score Matching Analysis. Reviews in Cardiovascular Medicine. 24(9), 267.
30. Luo, L., Zhou, L. and Song, P.X.K.* (2023). Real-time regression analysis for streaming clustered data with possible abnormal data batches. Journal of the American Statistical Association. 118 (543), 2029–2044.
29. Zhou, L., She, Xi. and Song, P.X.K. (2023). Distributed empirical likelihood approach to integrating unbalanced data. Statistica Sinica. 33, 2209–2231.
28. Zhou, L. and Song, P.X.-K. (2022). A discussion on “A selective review of statistical methods using calibration information from similar studies”. Statistical Theory and Related Fields. 6(3), 196–198.
27. Jansen, E., Corcoran, K.., Perng, W., Dunietz, G., Cantoral, A., Zhou, L., Tellez-Rojo, M. and Peterson, K. (2022). Relationships of beverage consumption and actigraphy-assessed sleep parameters among urban-dwelling youth from Mexico. Public Health Nutrition. 25(7),1844–1853.
26. Tan, X., Chang, C., Zhou, L., and Tang, L.(2022). A tree-based model averaging approach for personalized treatment effect estimation from heterogeneous data sources. Proceedings of the 39th International Conference on Machine Learning, PMLR. 162:21013-21036.
25. Zhou, L., Sun, S., Hu, H. and Song, P.X.K.(2022). Subgroup-effects models for the analysis of personal treatment effects. The Annals of Applied Statistics.16(1), 80–103.
24. Wang, F., Zhou, L., Tang, L. and Song, P.X.K.(2021). Method of contraction-expansion for simultaneous inference in linear models. Journal of Machine Learning Research. 22(192), 1–32.
23. Tang, L., Zhou, L., and Song, P.X.K.(2020). Distributed simultaneous inference in generalized linear models via confidence distribution. Journal of Multivariate Analysis. 176, 104567.
22. LaBarre, J., Puttabyatappa, M., Song, P.X.K., Goodrich, J., Zhou, L., Rajendiran, T., Soni,T., Domino, S., Treadwell, M., Dolinoy, D., Padmanabhan, V., and Burant, C.(2020). Maternal lipid levels across pregnancy impact the umbilical cord blood lipidome and infant birth weight. Scientific Reports. 10:14209.
21. LaBarre, J., Peterson, K.E., Kachman, M.T., Perng, W., Tang, L., Hao, W., Zhou, L.,Karnovsky, A., Gantoral, A., Tellez-Rojo, M.M., Song, P.X.K., and Burant, C.F. (2020). Mitochondrial nutrient utilization underlying the association between metabolites and insulin resistance in adolescents. J. Clin. Endocrinal. Metab. 105(7), 2442-2455.
20. LaBarre, J., Peterson, K., Hao, W., Kachman, M., Tang, L., Perng, W., Zhou, L., Song, P.X.-K., Karnovsky, A., Cantoral, A., Tellez-Rojo, M. and Burant, C. (2019). Intrinsic Mitochondrial Nutrient Utilization May Underlie the Association of Metabolite Levels with BMIz and Insulin Resistance (FS03-02-19). Current Developments in Nutrition 3, nzz046.FS03-02-19.
19. Corcoran, K., Peterson, K., Perng, W., Dunietz, G., Cantoral, A., Zhou, L., Tellez-Rojo, M.and Jansen, E. (2019). Adolescent Beverage Intake in Relation to Actigraphy-assessed Sleep Duration, Timing, and Fragmentation (P18-100-19). Current Developments in Nutrition 3 nzz039.P18-100-19.
18. Zhou, L., Lin, H.*, Chen, K., and Liang, H. (2019). Efficient estimation and computation of parameters and nonparametric functions in generalized semi/non-parametric regression models. Journal of Econometrics. 213, 593–607.
17. Zhou, L., Li, H., Lin, H., and Song, P. X. K. (2019). Evaluation of functional covariateenvironment interaction in the Cox model. The Canadian Journal of Statistics 47, 204–221.
16. Tang, L., Zhou, L., and Song, P. X. K. (2019). Fusion learning algorithm to combine partially heterogeneous Cox models. Computational Statistics 34, 395–414.
15. Lin, H., Yang, B., Zhou, L., YIP., P., Chen, Y., and Liang, H. (2019). Global kernel estimator and test of varying-coefficient autoregressive model. The Canadian Journal of Statistics 47(3),487–519.
14. Zhou, L., Lin, H., and Liang, H. (2018). Efficient estimation of the nonparametric mean and covariance functions for longitudinal and sparse functional data. Journal of the American Statistical Association 113, 1550–1564.
13. Li, Y., Wang, S., Song, P. X. K., Wang, N., Zhou, L., and Zhu, J. (2018). Doubly regularized estimation and selection in linear mixed-effects models for high-dimensional longitudinal data. Statistics and Its Interface 11, 721–737.
12. Tang, L., Chaudhuri, S., Bagherjeiran, A., and Zhou, L. (2018). Learning large scale ordinal ranking model via divide-and-conquer technique. WWW2018, April 23-27, 2018, Lyon, France.
11. Jansen, E. C., Zhou, L.#, Song, P. X. K., Sanchez, B. N., Mercado, A., Hu, H., Solano, M., Peterson, K. E., and Tellez-Rojo M. M. (2018). Prenatal lead exposure in relation to age at menarche: results from a longitudinal study in Mexico City. Journal of Developmental Origins of Health and Disease 9, 467–472 (#Co-first author).
10. Jansen, E. C., Zhou, L., Perng, W., Song, P. X. K., Tellez-Rojo, M. M., Mercado, A., Peterson, K. E., and Cantoral, A. (2018). Vegetable and lean proteins-based and processed meat and refined grains pattern-based dietary patterns in early childhood are associated with pubertal timing in a sex-specific manner: A prospective study of children from Mexico City. Nutrition Research. 56, 41–50.
9. Lin, H., Zhou, F., Wang, Q., Zhou, L., and Qin, J. (2018). Robust and efficient estimation for the treatment effect in causal inference and missing data problems. Journal of Econometrics. 205, 363–380.
8. Lin, H., Zhou, L., and Wang, B. (2017). Generalized partial linear models with unknown link and unknown baseline functions for longitudinal data. Statistica Sinica. 27, 1281–1298.
7. Zhou, L., Tang, L., Song, A. T., Cibrik, D. M., and Song, P. X. K. (2017). A LASSO method to identify protein signature predicting post-transplant renal graft survival. Statistics in Biosciences. 9(2), 431–452.
6. Zhou, L., Lin, H., and Lin Y. C. (2016). Education, intelligence, and well-being: Evidence from a semiparametric latent variable transformation model for multiple outcomes of mixed types. Social Indicators Research. 125(3), 1011–1033.
5. Lin, H., Zhou, L., Li, C., and Li, Y. (2014). Semiparametric transformation models for semicompeting survival data. Biometrics 70, 599–607.
4. Zhou, L., Lin, H., Song, X. and Li, Y. (2014). Selection of latent variables for multiple mixed-outcome models. Scandinavian Journal of Statistics. 41, 1064–1082.
3. Lin, H., Zhou, L., and Zhou, X. (2014). Semiparametric regression analysis of longitudinal skewed data. Scandinavian Journal of Statistics. 41, 1031–1050.
2. Lin, H., Zhou, L., Elashof, R. M., and Li, Y. (2014). Semiparametric latent variable transformation models for multiple mixed outcomes. Statistica Sinica. 24, 833–854.
1. Lin, H., Zhou, L., Peng, H., and Zhou, X. H. (2011). Selection and combination of biomarkers using ROC method for disease classification and prediction. Canadian Journal of Statistics.39, 324–343.
Software
RCD: Python code in the form of Map-Reduce functions for conducting statistical inference based on estiamting functions. The package has been tested on the University of Michigan Flux Hadoop cluster and support HDFS file format.
MODAC: Both R and Python packages are provided. Map-Reduce functions are coded to fit generalized linear models on Hadoop clusters.
