My research interests lie in functional, longitudinal, and geometrical data analysis. I also work on statistical applications such as remote sensing, plant genomics, and neuroscience. This is my Google Scholar page.


  1. (Submitted) Qiu, J., Dai, X. and Zhu, Z. (2020) Nonparametric estimation of repeated densities with heterogeneous sample sizes.
  2. (Submitted) Dai, X. (2020) Statistical inference on the Hilbert sphere with application to random densities.
  3. (Revising) Zhu, W., Zhu, Z. and Dai, X. (2019) Spatiotemporal Satellite Data Imputation Based on Sparse Functional Data Analysis.


Statistical Methods and Theory

  1. (Accepted) Dai, X., Lin, Z. and Müller, H.-G. (2020) Modeling longitudinal data on Riemannian manifolds. Biometrics.
  2. Dai, X. and Müller, H.-G. (2018) Principal component analysis for functional Data on Riemannian manifold and spheres. The Annals of Statistics, 46, 3334–3361.   PDF   Supplement
  3. Dai, X., Müller, H.-G. and Tao, W. (2018) Derivative principal components for representing the time dynamics of longitudinal and functional data. Statistica Sinica, 28, 1583–1609.   PDF
  4. Dai, X., Müller, H.-G. and Yao, F. (2017) Optimal Bayes classifiers for functional data and density ratios. Biometrika, 104, 545–560.   PDF   Supplement


  1. Li, H., Wang, L., Luo, M.-C., Nie, F., Zhou, Y., McGuire, P.E., et al. (2019) Recombination between Homoeologous Chromosomes Induced in Durum Wheat by the Aegilops Speltoides Su1-Ph1 Suppressor. Theoretical and Applied Genetics.   Link
  2. Dai, X., Müller, H.-G., Wang, J.-L. and Deoni, S.C.L. (2019) Age-Dynamic Networks and Functional Correlation for Early White Matter Myelination. Brain Structure & Function, 224, 535–551.   Link
  3. Xu, J., Dai, X., Ramasamy, R.K., Wang, L., Zhu, T., McGuire, P.E., et al. (2019) Aegilops tauschii Genome Sequence: A Framework for Meta-Analysis of Wheat QTLs. G3: Genes, Genomes, Genetics, 9, 841–853.   Link
  4. Dai, X., Hadjipantelis, P., Wang, J.-L., Deoni, S.C.L. and Müller, H.-G. (2019) Longitudinal Associations between White Matter Maturation and Cognitive Development across Early Childhood. Human Brain Mapping, 40, 4130–4145.   Link
  5. Dai, X., Wang, H., Zhou, H., Wang, L., Dvořák, J., Bennetzen, J., et al. (2018) Birth and death of LTR retrotransposons in Aegilops tauschii. Genetics, 210, 1039–1051.   Link
  6. Dvorak, J., Wang, L., Zhu, T., Jorgensen, C.M., Deal, K.R., Dai, X., et al. (2018) Structural variation and rates of genome evolution in the grass family seen through comparison of sequences of genomes greatly differing in size. The Plant Journal, 95, 487–503.   Link
  7. Luo, M.-C., Gu, Y.Q., Puiu, D., Wang, H., Twardziok, S.O., Deal, K.R., et al. (2017) Genome sequence of the progenitor of the wheat D genome Aegilops tauschii. Nature, 551, 498–502.   Link