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  1. 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, vol 372, Issue 6548,  eabf3736, 2021.
  2. Fan, Z., Luo, S., Qie, X., Ye, J., and  Zhu, H. Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms. Journal of American Statistical Association, 2021, in press.
  3. Liu, R.J.,   and Zhu, H.T.  Statistical disease mapping for heterogeneous neuroimaging studies (with discussions). The Canadian Journal of Statistics, 49, 1, 10–34, 2021.
  4. Shu, H., Wang, X. and Zhu, H. D-CCA: A Decomposition-based Canonical Correlation Analysis for High-Dimensional Datasets. Journal of American Statistical Association, 115, 292-306 , 2020.
  5. Kong,D. 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, 115, 403-424, 2020.
  6. 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, 51, 1637-1644, 2019.
  7. Z. Zhang, M.  Descoteaux, J.  Zhang, G.  Girard, M.  Chamberland, D. Dunson,  A.  Srivastava, and H. Zhu.  Mapping Population based Structural  Connectomes. NeuroImage, 172, 130-145, 2018.
  8. Cornea, E., Zhu, H.T., Kim, P. and Ibrahim, J. G. Intrinsic regression model for data in Riemannian symmetric space. JRSS, Series B, 79, 463-482, 2017.
  9. 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, 118, 613-627, 2015.
  10. 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, 109, 977-990, 2014.
  11. Zhou, H., Li, L.X., and Zhu, H.T. Tensor regression with applications in neuroimaging data analysis. Journal of American Statistical Association, 540-552, 2013.
  12. Zhu, HT., Li, R., and Kong, L. Multivariate varying coefficient model and its application in neuroimaging data. Annals of Statistics, 40, 2634-2666, 2012.
  13. Yuan, Y., Zhu, H.T., Lin, W. L., and Marron, J. S. Local polynomial regression for symmetric positive definitive matrices. JRSS, Series B, 74, 697-719, 2012.
  14. Ibrahim, J. G., Zhu, H.T., Garcia, R. I., Guo, R.X. Fixed and random effects selection for generalized mixed effects models, Biometrics, 67, 495-503, 2011
  15. Zhu, HT., Kong, L.,  Li, R., Styner, M.,  Gerig, G., Lin, W. and  Gilmore, J. H.  FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics, NeuroImage, 56, 1412-1425, 2011.
  16. Li, YM,   Zhu HT,  Shen DG, Lin WL, Gilmore J, and Ibrahim JG. Multiscale adaptive regression models for neuroimaging data. JRSS, Series B, 73, 559-578, 2011.
  17. 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, 102, 1081-1110, 2007.
  18. Zhu HT, Ibrahim, JG, Lee SY, and Zhang HP. Appropriate perturbation and influence measures in local influence. Annals of Statistics, 35, 2565-2588, 2007.
  19. 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, 63:339-355, 2001.
  20. Zhu HT and Lee SY. Local influence for models with incomplete data. Journal of the Royal Statistical Society, Series B, 63:111-126, 2001.