Application

My work applies to statistical genetics and genomics, epidemiology, electronic health records, and climate data. Below are some selected papers.

underline indicates a student working under my (co)supervision, with ^♦ denoting an undergraduate student mentee; ✉ indicates the corresponding author.

Statistical Genetics

• Liu, Y. and Wang, T.✉ (2025+). “A powerful transformation of quantitative responses for biobank-scale association studies”, under review.
• Jiang, R., and Wang, T.✉ (2025+). “A Minimax Optimal Quantile Rank Score Test”, under review.
• Wang, T.✉, Ionita-Laza, I., and Wei, Y. (2024). “A unified quantile framework for nonlinear heterogeneous transcriptome-wide associations”, Annals of Applied Statistics, accepted.
• 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 (1), 6460.
• Wang, T.✉, Ionita-Laza, I., and Wei, Y. (2022). “Integrated Quantile RAnk Test (iQRAT) for gene-level associations”. Annals of Applied Statistics, 16 (3), 1423 - 1444.
• Wang, T., Liu, J., and Wu, A. (2024). “Semiparametric Analysis in Case-Control Studies for Gene-Environment Independent Models: Bibliographical Connections and Extensions”, Journal of Data Science, accepted.
• Wang, T.✉ and Asher, A. (2021). “Improved Semiparametric Analysis of Polygenic Gene-Environment Interactions in Case-Control Studies”. Statistics in Biosciences, 13, 386–401.

Microbiome Studies

• Li, Z.^♦, and Wang, T.✉ (2025+). “Tree-aggregation of high-dimensional compositional data subject to measurement errors”, under review.
• Wang, Z., Ling, W., and Wang, T.✉ (2025). “A Semiparametric Quantile Regression Rank Score Test for Zero-inflated Data”, Biometrics, accepted.
• Zhao, H., and Wang, T.✉ (2024). “A high-dimensional calibration method for log-contrast models subject to measurement errors”, Biometrics, accepted.
• Wang, Z., and Wang, T.✉ (2024). “A Semiparametric Quantile Single-Index Model for Zero-Inflated Outcomes”, Statistica Sinica, accepted.
• Jiang, R.^♦, Zhan, X.✉, and Wang, T.✉ (2023). “A Flexible Zero-Inflated Poisson-Gamma Model with Application to Microbiome Read Count Data”, Journal of the American Statistical Association, 118 (542), 792 - 804.
• Wang, T., Ling, W., Plantinga, A., Wu, M., and Zhan, X. (2022). “Testing microbiome association using integrated quantile regression models”. Bioinformatics, 38(2), 419-425.

EHR Data

• Zhao, H., and Wang, T.✉ (2025+). “Generalizing Transfer Learning: A Flexible Doubly Robust Estimation Approach for Missing Data”, under review.
• Zhao, H., and Wang, T.✉ (2025+). “Doubly robust augmented model transfer inference with completely missing covariates”, under review.
• Wang, T.✉, Ma, Y, and Wei, Y. (2025+). “Time-varying Quantile Regression with Multi-outcome Latent Groups”, under review.
• Zhao, H., and Wang, T.✉ (2025+). “A simulation-free extrapolation method for misspecified models with errors-in-variables”, under review.
• Blas Achic, B.^♯, Wang, T.^♯ , Su, Y., Kipnis, V., Dodd, K., and Carroll, R. J. (2018). “Categorizing a Continuous Predictor Subject to Measurement Error”. Electronic Journal of Statistics, Vol. 12, No. 2, 4032-4056. ( ^♯ joint first authors).
• Wang, T.✉, Zhang, W., and Wei, Y. (2024). “ZIKQ: An innovative centile chart method for utilizing natural history data in rare disease clinical development”, Statistica Sinica, accepted.

Climate Modeling

• Li, Y., Wang, T.✉, Yan, J., and Zhang, X. (2025). “Improved Optimal Fingerprinting Based on Estimating Equations Reaffirms Anthropogenic Effect on Global Warming”, Journal of Climate, 38(8), 1779-1790.
• Lau, Y., Wang, T.✉, Yan, J., and Zhang, X. (2023). “Extreme Value Modeling with Errors-in-Variables in Detection and Attribution of Changes in Climate Extremes”, Statistics and Computing, 33 (6), 125.
• Ma, S., Wang, T.✉, Yan, J., and Zhang, X. (2023). “Optimal Fingerprinting with Estimating Equations”, Journal of Climate, 36(20), 7109-7122.

Others

• 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, 1-53.
• Wang, Y., and Wang, T.✉ (2025+). “Multi-Group Quadratic Discriminant Analysis via Projection”, under review.
• Gaynanova, I. and Wang, T. (2019). “Sparse quadratic classification rules via linear dimension reduction”. Journal of Multivariate Analysis, 169, 278–299.

Research opportunities are open to highly motivated students. Interested individuals are encouraged to reach out for more details.