My research interest lies in the analysis of complex data objects such as functional, longitudinal, and geometrical data. These complex data types, collectively referred to as object data, are generated in increasing volume and variety in modern data collection processes, calling for new analytic approaches. A major theme in my methodological and theoretical research is to address the infinite-dimensional curve nature of functional and longitudinal data, and the geometrical constraint in non-Euclidean data. Tightly integrated in my research are statistical applications in the fields of developmental neuroscience, remote sensing, and plant phenomics and genomics, etc.

This is my Google Scholar page. My research is supported by NSF and NIH.

Manuscripts

*student first author under my supervision

  1. Chang, X.*, Zhu, Z., Dai, X. and Hobbs, J. (2021) A geospatial functional model for OCO-2 data with applications in imputation and land fraction estimation.   ArXiv
  2. Kusmec, A., Attigala, L., Dai, X., Srinivasan, S., Yeh, C.-T. and Schnable, P.S. (2021) Genetic adaptation to heat stress in US hybrid maize and further adaptation to a changing climate.
  3. Chang, X., Dai, X. and Zhu, Z. (2021) Functional change-point detection for sparse multivariate functional data, with application to urban dynamics.
  4. Cataldo, A.G., Dai, X. and Müller, H.-G. (2021) Distributional representation of longitudinal data: Visualization, regression and prediction.   ArXiv
  5. Yeon, H.*, Dai, X. and Nordman, D.J. (2021) Bootstrap inference in functional linear regression models with scalar response.

Publications

Statistical Methods and Theory

  1. Qiu, J.*, Dai, X. and Zhu, Z. (2022) Nonparametric Estimation of Repeated Densities with Heterogeneous Sample Sizes. Journal of American Statistical Association, accepted.   PDF   Supplement
  2. Zhu, W., Zhu, Z. and Dai, X. (2021) Spatiotemporal Satellite Data Imputation Based on Sparse Functional Data Analysis. Annals of Applied Statistics, accepted.
  3. Dai, X. and Lopez-Pintado, S. (2021) Tukey’s depth for object data. Journal of the American Statistical Association, accepted.   PDF   Supplement
  4. Dai, X. (2022) Statistical Inference on the Hilbert Sphere with Application to Random Densities. Electronic Journal of Statistics, 16, 700–736.   Link
  5. Dai, X., Lin, Z. and Müller, H.-G. (2021) Modeling sparse longitudinal data on Riemannian manifolds. Biometrics, 77, 1328–1341.   PDF   Supplement
  6. 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   Correction
  7. 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
  8. Dai, X., Müller, H.-G. and Yao, F. (2017) Optimal Bayes classifiers for functional data and density ratios. Biometrika, 104, 545–560.   PDF   Supplement

Applications

  1. Kim, H.K., Dai, X., Lu, S.-H., Lu, T.-W. and Chou, L.-S. (2022) Discriminating features of ground reaction forces in overweight old and young adults during walking using functional principal component analysis. Gait & Posture, 94, 166–172.
  2. 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
  3. 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
  4. 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
  5. 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
  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. 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
  8. 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