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

• 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.