Statistical Methodology

I develop statistical theory and methods for detecting weak signals and characterizing heterogeneous associations. I also work on statistical learning and data fusion with missing variables, measurement error, and differences across study populations.

Selected methodological papers are listed below. For a full list of publications, see the Publications page.

Underlining identifies current or former research mentees; ^♦ marks work initiated during undergraduate mentoring; ✉ indicates a corresponding author.

Distribution-sensitive inference for weak signals and heterogeneous associations

• Liu, Y. and Wang, T.✉ (2026). “A powerful transformation of quantitative responses for biobank-scale association studies”, Journal of the American Statistical Association, 121(553), 678-689.

• Wang, Z.^♦ and Wang, T.✉ (2026). “A semiparametric quantile single-index model for zero-inflated outcomes”, Statistica Sinica, 36(4), 2003-2024.

• Wang, T.✉, Ionita-Laza, I., and Wei, Y. (2025). “A unified quantile framework for nonlinear heterogeneous transcriptome-wide associations”, The Annals of Applied Statistics, 19(2), 967-985.

• Wang, Z.^♦, Ling, W., and Wang, T.✉ (2025). “A semiparametric quantile regression rank score test for zero-inflated data”, Biometrics, 81(2), ujaf050.

• Wang, F., Wang, C., Wang, T., Masala, M., Fiorillo, E., Devoto, M., Cucca, F., and Ionita-Laza, I. (2025). “Computationally efficient whole-genome quantile regression at biobank scale”, Proceedings of the National Academy of Sciences of the United States of America, 122(50), e2513007122.

• Wang, C., Wang, T., Kiryluk, K., Wei, Y., Aschard, H., and Ionita-Laza, I. (2024). “Genome-wide discovery for biomarkers using quantile regression at biobank scale”, Nature Communications, 15, 6460.

• Wang, T.✉, Ionita-Laza, I., and Wei, Y. (2022). “Integrated quantile rank test (iQRAT) for gene-level associations”, The Annals of Applied Statistics, 16(3), 1423-1444.

Manuscripts under review

• Wang, T.✉, Ma, Y., and Wei, Y. (2026). “Joint estimation and grouping of quantile association curves across multiple responses”. Under review.

Statistical learning and data fusion

• Zhao, H. and Wang, T.✉ (2026). “Augmented transfer regression learning for completely missing covariates”, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 1-18.

• Wang, Y. and Wang, T.✉ (2026). “Multi-group quadratic discriminant analysis via projection”, Journal of Computational and Graphical Statistics, 1-22.

• Zhao, H. and Wang, T.✉ (2026). “Simulation-free extrapolation for misspecified models induced by categorizing an error-prone continuous covariate”, Statistical Science, accepted.

• Zhao, H. and Wang, T.✉ (2026). “Doubly robust transfer learning under sub-group shift for cohort-level missing indicator covariates”, Statistica Sinica, accepted.

• Zhao, H. and Wang, T.✉ (2024). “Debiased high-dimensional regression calibration for errors-in-variables log-contrast models”, Biometrics, 80(4), ujae153.

• Zhou, S., Pati, D., Wang, T., Yang, Y., and Carroll, R. J. (2023). “Gaussian processes with errors in variables: Theory and computation”, Journal of Machine Learning Research, 24(87), 1-53.

Manuscripts under review

• Li, Z.^♦ and Wang, T.✉ (2026). “Tree-aggregated compositional regression under measurement error”. Under review.

• Wang, T.✉ (2026). “Geometry of tail allocation in conformal prediction intervals”. Under review.

• Zhao, H., Liu, M.✉, and Wang, T.✉ (2026). “Data fusion for errors-in-variables”. Under review.

• Wang, Z., Guo, Z., Wang, T.✉, and Liu, M.✉ (2026). “Multi-source domain adaptation via alignment-guided distributionally robust learning”. Under review.