{"id":3969,"date":"2021-07-02T21:57:23","date_gmt":"2021-07-03T01:57:23","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2\/?page_id=3969"},"modified":"2025-05-29T11:47:29","modified_gmt":"2025-05-29T15:47:29","slug":"highlighted-publications","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/highlighted-publications\/","title":{"rendered":"Highlighted Publications"},"content":{"rendered":"
    \n
  1. Wen Q., Shi C., Yang Y., Tang N., Zhu H. Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments. ICML 2025.<\/li>\n
  2. Zhao B., Zheng S., Zhu H. On blockwise and reference panel-based estimators for genetic data prediction in high dimensions. The Annals of Statistics 52 (3), 948-965, 2024.<\/li>\n
  3. Jiang Y., Zhao B., Wang X., Tang B., Peng H., Luo Z., Shen Y., Wang Z., Jiang Z., Wang J., Ye J., Wang X., Zhu H. UKB-MDRMF: A Multi-Disease Risk and Multimorbidity Framework Based on UK Biobank Data. Nature Communication, in press, 2025.<\/li>\n
  4. Li, T., Shi, C., Lu, Z., Li, Y., & Zhu, H. Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing. Journal of the American Statistical Association, 00, 1-15, 2024. [PDF<\/a>]<\/li>\n
  5. Luo, S., Yang, Y., Shi, C., Yao, F., Ye, J., & Zhu, H. Policy evaluation for temporal and\/or spatial dependent experiments. Journal of the Royal Statistical Society Series B: Statistical Methodology,<\/em> 00, 1\u201327, 2024. [PDF<\/a>]<\/li>\n
  6. Zhao, B., Li, T., Li, Y., Fan, Z., Xiong, D., Wang, X., Gao, M., Smith, S.M., & Zhu, H. (2023). An atlas of trait associations with resting-state and task-evoked human brain functional organizations in the UK Biobank. Imaging Neuroscience.<\/em> Advance Publication. [PDF<\/a>]<\/li>\n
  7. Zhao, B., Li, T., Fan, Z., Yang, Y., Shu, J., Yang, X., Wang, X., Luo, T., Tang, J., Xiong, D., Wu, Z., Li, B., Chen, J., Shan, Y., Tomlinson, C., Zhu, Z., Li, Y., Stein, J. L., & Zhu, H. Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images. Science<\/em>, 380(6648), abn6598, 2023. [PDF<\/a>]<\/li>\n
  8. Zhang, Z., Wu, Y., Xiong, D., Ibrahim, J. G., Srivastava, A., & Zhu, H. LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures (with discussion). Journal of the American Statistical Association<\/em>,<\/span> 118:541,<\/span> 3-17<\/span>, 2023.\u00a0[PDF<\/a>]<\/li>\n
  9. Zhu, H., Li, T., & Zhao, B. Statistical learning methods for neuroimaging data analysis with applications. Annual Review of Biomedical Data Science, Volume 6, Issue 1, 2023.<\/i> [PDF<\/a>]<\/li>\n
  10. Qin, Z. (Tony), Zhu, H., & Ye, J. (2022). Reinforcement learning for ridesharing: An extended survey. Transportation Research Part C: Emerging Technologies<\/em>, 144, 103852.\u00a0[PDF<\/a>]<\/li>\n
  11. Zhao, B., Li, T., Smith, S. M., Xiong, D., Wang, X., Yang, Y., Luo, T., Zhu, Z., Shan, Y., Matoba, N., Sun, Q., Yang, Y., Hauberg, M. E., Bendl, J., Fullard, J. F., Roussos, P., Lin, W., Li, Y., Stein, J. L., & Zhu, H. Common variants contribute to intrinsic human brain functional networks. Nature Genetics,\u00a054<\/em>(4), 508\u2013517, 2022. [PDF<\/a>]<\/li>\n
  12. B. Zhao and H. Zhu. On genetic correlation estimation with summary statistics from genome-wide association studies. Journal of American Statistical Association<\/em>, 2022;117(537):1-11. [PDF<\/a>]<\/li>\n
  13. B. Zhao, T. Li, Y. Yang, X. Wang, T. Luo, Y.Shan, Z. Zhu, D. Xiong, M. E. Hauberg, J. Bendl, J.F. Fullard, P. Roussos, Y. Li, J. L. Stein, and H Zhu. Common genetic variation influencing human white matter microstructure. Science<\/em>, vol 372, Issue 6548, eabf3736, 2021. [PDF<\/a>]<\/li>\n
  14. Fan, Z., Luo, S., Qie, X., Ye, J., and\u00a0 Zhu, H. Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms. Journal of American Statistical Association<\/em>, 2021, Volume 116, Issue 536, pp. 1688 – 1699. [PDF<\/a>]<\/li>\n
  15. Liu, R.J., \u00a0\u00a0and Zhu, H.T. Statistical disease mapping for heterogeneous neuroimaging studies (with discussions). The Canadian Journal of Statistics<\/em>, 49, 1, 10\u201334, 2021. [PDF<\/a>]<\/li>\n
  16. Shu, H., Wang, X. and Zhu, H. D-CCA: A Decomposition-based Canonical Correlation Analysis for High-Dimensional Datasets. Journal of American Statistical Association<\/em>, 115, 292-306\u00a0, 2020. [PDF<\/a>]<\/li>\n
  17. Kong\uff0cD. H., An, B. G., Zhang, J. W., and Zhu, H. L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses. Journal of American Statistical Association<\/em>, 115, 403-424, 2020. [PDF<\/a>]<\/li>\n
  18. Bingxin Zhao, Tianyou Luo, Tengfei Li, Yun Li, Jingwen Zhang, Yue Shan, Xifeng Wang, Liuqing Yang, Fan Zhou, Ziliang Zhu, Hongtu Zhu. GWAS of 19,629 individuals identifies novel genetic variants for regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Featured on the cover of Nature Genetics<\/em>, 51, 1637-1644, 2019. [PDF<\/a>]<\/li>\n
  19. Z. Zhang, M.\u00a0 Descoteaux, J.\u00a0 Zhang, G.\u00a0 Girard, M.\u00a0 Chamberland, D. Dunson,\u00a0 A.\u00a0 Srivastava, and H. Zhu. \u00a0Mapping Population based Structural Connectomes. NeuroImage<\/em>, 172, 130-145, 2018. [PDF<\/a>]<\/li>\n
  20. Cornea, E., Zhu, H.T., Kim, P. and Ibrahim, J. G. Intrinsic regression model for data in Riemannian symmetric space. JRSS, Series B<\/em>, 79, 463-482, 2017. [PDF<\/a>]<\/li>\n
  21. M.Huang, T.Nichols, C.Huang, Y.Yang, Z. Lu, Q. Feng, R.C. Knickmeyer, H.Zhu, and for ADNI. FVGWAS: Fast Voxelwise Genome Wide Association Analysis of Large-scale Imaging Genetic Data. NeuroImage<\/em>, 118, 613-627, 2015. [PDF<\/a>]<\/li>\n
  22. Zhu, H.T., Fan, J.Q., and Kong, L.L. Spatially varying coefficient models with applications in neuroimaging data with jumping discontinuity. Journal of American Statistical Association<\/em>, 109, 977-990, 2014. [PDF<\/a>]<\/li>\n
  23. Zhou, H., Li, L.X., and Zhu, H.T. Tensor regression with applications in neuroimaging data analysis. Journal of American Statistical Association<\/em>, 540-552, 2013. [PDF<\/a>]<\/li>\n
  24. Zhu, HT., Li, R., and Kong, L. Multivariate varying coefficient model and its application in neuroimaging data. Annals of Statistics<\/em>, 40, 2634-2666, 2012. [PDF<\/a>]<\/li>\n
  25. Yuan, Y., Zhu, H.T., Lin, W. L., and Marron, J. S. Local polynomial regression for symmetric positive definitive matrices. JRSS, Series B<\/em>, 74, 697-719, 2012. [PDF<\/a>]<\/li>\n
  26. Ibrahim, J. G., Zhu, H.T., Garcia, R. I., Guo, R.X. Fixed and random effects selection for generalized mixed effects models, Biometrics<\/em>, 67, 495-503, 2011. [PDF<\/a>]<\/li>\n
  27. Zhu, HT., Kong, L.,\u00a0 Li, R., Styner, M.,\u00a0 Gerig, G., Lin, W. and \u00a0Gilmore, J. H. FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics, NeuroImage<\/em>, 56, 1412-1425, 2011. [PDF<\/a>]<\/li>\n
  28. Li, YM,\u00a0\u00a0 Zhu HT,\u00a0 Shen DG, Lin WL, Gilmore J, and Ibrahim JG. Multiscale adaptive regression models for neuroimaging data. JRSS, Series B<\/em>, 73, 559-578, 2011. [PDF<\/a>]<\/li>\n
  29. Zhu HT, Zhang HP, Ibrahim JG, and Peterson BG. Statistical analysis of diffusion tensors in diffusion-weighted magnetic resonance image data (with discussion). Journal of the American Statistical Association<\/em>, 102, 1081-1110, 2007. [PDF<\/a>]<\/li>\n
  30. Zhu HT, Ibrahim, JG, Lee SY, and Zhang HP. Appropriate perturbation and influence measures in local influence. Annals of Statistics<\/em>, 35, 2565-2588, 2007. [PDF<\/a>]<\/li>\n
  31. Gu MG and Zhu HT. Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation. Journal of the Royal Statistical Society, Series B<\/em>, 63:339-355, 2001. [PDF<\/a>]<\/li>\n
  32. Zhu HT and Lee SY. Local influence for models with incomplete data. Journal of the Royal Statistical Society, Series B<\/em>, 63:111-126, 2001. [PDF<\/a>]<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Wen Q., Shi C., Yang Y., Tang N., Zhu H. Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments. ICML 2025. Zhao B., Zheng S., Zhu H. On blockwise and reference panel-based estimators for genetic data prediction in high dimensions. The Annals of Statistics 52 (3), 948-965, 2024. Jiang Y., Zhao B., … Read more<\/a><\/p>\n","protected":false},"author":1503,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-3969","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"\nHighlighted Publications - BIG-S2<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Highlighted Publications - BIG-S2\" \/>\n<meta property=\"og:description\" content=\"Wen Q., Shi C., Yang Y., Tang N., Zhu H. Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments. ICML 2025. Zhao B., Zheng S., Zhu H. 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Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments. ICML 2025. Zhao B., Zheng S., Zhu H. On blockwise and reference panel-based estimators for genetic data prediction in high dimensions. The Annals of Statistics 52 (3), 948-965, 2024. 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