Publication

* trainee or student     # equal contribution     † corresponding author

Journals

• Lin, Y.* and Lin, Z.† (2025+). Binary regression and classification with covariates in metric spaces. Biometrics. 84(3): ujaf123. [arXiv]
• Lin, Y.*, Guo, Z., Sun, B., and Lin, Z.† (2025). Testing high-dimensional mediation effect with arbitrary exposure-mediator coefficients. TEST. 34: 530–579. [arXiv]
• Hu, X.* and Lin, Z.† (2025). Two-sample distribution tests in high dimensions via max-sliced Wasserstein distance and bootstrapping. Biometrika. 112(2): asaf001.
• Lin, Y.* and Lin, Z.† (2025). Hypothesis testing for functional linear models via bootstrapping. Bernoulli. 31(4): 3309-3330. [arXiv]
• Choi, C., Lin, Z., and Park, B. U. (2025). High-dimensional partially linear additive models on Riemannian manifolds. Bernoulli. 31(4): 3285-3308.
• Quan, M.*^# and Lin, Z.^#† (2024). Optimal one-pass nonparametric estimation under memory constraint. Journal of the American Statistical Association. 119(596): 285–296. [arXiv]
• Zhou, H.*, Lin, Z., and Yao, F. (2024+). Intrinsic Wasserstein correlation analysis. Statistica Sinica. [arXiv]
• Lin, Z., Kong, D., and Wang, L. (2023). Causal inference on distribution functions. Journal of the Royal Statistical Society: Series B. 85(2): 378–398. [arXiv]
• Lin, Z.^†, Lopes, M., and Müller, H.-G. (2023). High-dimensional MANOVA via bootstrapping and its application to functional and sparse count data. Journal of the American Statistical Association. 118(541):177–191. [arXiv][code]
• Lin, Z.^†, Müller, H.-G., and Park, B. U. (2023). Additive models for symmetric positive-definite matrices and Lie groups. Biometrika. 110(2): 361–379. [arXiv]
• Chen, Y., Lin, Z., and Müller, H.-G. (2023). Wasserstein regression. Journal of the American Statistical Association. 118(542): 869–882. [arXiv]
• Shao, L.*^#, Lin, Z.^#, and Yao, F. (2022). Intrinsic Riemannian functional data analysis for sparse longitudinal observations. The Annals of Statistics. 50(3): 1696–1721. [arXiv]
• Lin, Z.^† and Wang, J.-L. (2022). Mean and covariance estimation for functional snippets. Journal of the American Statistical Association. 117(537): 348–360. [arXiv][code]
• Guan, T., Lin, Z., Groves, K. and Cao, J. (2022). Sparse functional partial least squares regression with a locally sparse slope function. Statistics and Computing. 32(2):30.
• Lin, Z.^† and Müller, H.-G. (2021). Total variation regularized Fréchet regression for metric-space valued data. The Annals of Statistics. 49(6): 3510–3533. [arXiv]
• Dai, X., Lin, Z., and Müller, H.-G. (2021). Modeling sparse longitudinal data on Riemannian manifolds. Biometrics. 77(4):1328–1341. [arXiv]
• Lin, Z., Wang, J.-L., and Zhong, Q. (2021). Basis expansions for functional snippets. Biometrika. 108(3): 709–726. [arXiv][code]
• Lin, Z. and Yao, F. (2021). Functional regression on manifold with contamination. Biometrika. 108(1): 167–181. [arXiv]
• Guan, T.*, Lin, Z., and Cao, J. (2020). Estimating truncated functional linear models with a nested group bridge approach. Journal of Computational and Graphical Statistics. 29(3): 620–628. [arXiv]
• Lopez, M., Lin, Z., and Müller, H.-G. (2020). Bootstrapping max statistics in high dimensions: Near-parametric rates under weak variance decay and application to functional data and multinomial data. The Annals of Statistics. 48(2): 1214–1229. [arXiv][code]
• Lin, Z. (2019). Riemannian geometry of symmetric positive definite matrices via Cholesky decomposition. SIAM Journal on Matrix Analysis and Applications. 40(4): 1353–1370. [PDF][arXiv][code][bib]
• Lin, Z. and Yao, F. (2019). Intrinsic Riemannian functional data analysis. The Annals of Statistics.  47(6): 3533–3577. [arXiv][code][bib]
• Lin, Z., Müller, H.-G., and Yao, F. (2018). Mixture inner product spaces and their application to functional data analysis. The Annals of Statistics. 46(1): 370–400.
• Lin, Z., Cao, J., Wang, L., and Wang, H. (2017). Locally sparse estimator for functional linear regression models. Journal of Computational and Graphical Statistics. 26(2): 306–318.
• Lin, Z., Wang, L., and Cao, J. (2016). Interpretable functional principal component analysis. Biometrics. 72(3): 846–854.
• Lin, Z. and and Li, W. (2013). Restrictions of point estimate methods and remedy. Reliability Engineering & System Safety. 111(C):106–111.
• Li, W., Vaahedi, E., and Lin, Z. (2013). BC Hydro’s transmission reliability margin assessment in total transfer capability calculations. IEEE Transactions on Power Systems. 28(4):4796–4802.
• Lin, Z., Jiang, B., Pei, J., and Jiang, D. (2010). Mining discriminative items in multiple data streams. Journal of World Wide Web. 13(4):497–522.

Conferences

• Hu, X.*^#, Xue, G.*^#, Lin, Z., and Yu, Y. (2025). Fairness-aware Bayes optimal functional classification. NeurIPS 2025.
• Zhu, J.^#, Li, J*^#, Zhou, H., Lin, Y.*, Lin, Z.†, and Shi, C.† (2025). Balancing interference and correlation in spatial experimental designs: a causal graph cut approach. ICML 2025.
• Hu, X.* and Lin, Z. (2025). Transfer learning meets functional linear regression: no negative transfer under posterior drift. AAAI 2025 (acceptance rate 23.4%).
• Lin, Z.^† and Zhu, H. (2019). MFPCA: Multiscale functional principal component analysis. AAAI 2019.

Preprints

• Xue, G.*, Lin, Z., and Yu, Yi. (2024+). Optimal estimation in private distributed functional data analysis. [arXiv]
• Qui, R.* and Lin, Z.† (2024+). Semi-supervised Fréchet regression. Under revision. [arXiv]
• Hu, X.* and Lin, Z.† (2024+). A high-dimensional distribution-free two-sample test via projection Wasserstein distances and deep neural networks. Under revision.
• Zhu, J.*, Kong, D., Zhang, Z., and Lin, Z.† (2024+). Stationarity of manifold time series. Under revision. [arXiv]