Publications

My Google Scholar

With the increasingly large and complex data generated and need to be analyzed, statistics is highly interdisciplinary and rapidly expanding among the sciences. My career goal includes developing state-of-the-art statistical and machine learning methods for solving data-driven problems, advancing statistical theory in the emerging field of data science, and harnessing real-world evidence for decision-making and precision medicine. My primary research is centered around developing statistical theories and methodologies to promote statistical learning in complex data, especially on data heterogeneity and measurement errors. Much of my methodology research applies to genetic and genomic data analysis, microbiome data analysis, epidemiologic research, and environmental statistics.

I view open science practices as an important way to increase access and participation in academic research. Please check all open-source software related to my articles here.

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

Under Review

• Zhao, H. and Wang, T.✉ (2025+) “Doubly robust augmented model transfer inference with completely missing covariates”.
  • An earlier version won the 2025 IMS Hannan Graduate Student Travel Award

• Wang, T.✉ (2025+) “Optimal Conformal Prediction under Asymmetry: A Quantile Selection Approach”.

• Wang, T.✉, Ma, Y., and Wei, Y. (2025+) Time-varying Quantile Regression with Multi-outcome Latent Groups”.

2025

• Wang, Z., Ling, W., and Wang, T.✉ (2025). “A Semiparametric Quantile Regression Rank Score Test for Zero-inflated Data”, Biometrics, accepted.

• Wang, T.✉ (2025). “Moving Beyond Mean: Harnessing Big Data for Health Insights by Quantile Regression,” Big Data Analysis, Biostatistics and Bioinformatics (Eds. Chen, D. and Zhao, Y.), Springer, New York, in press.

• 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.

2024

• 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, 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.

• 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.

• Zhao, H., and Wang, T.✉ (2024). “A high-dimensional calibration method for log-contrast models subject to measurement errors”, Biometrics, 80(4), ujae153.

• Wang, Z.^♦ and Wang, T.✉ (2024). “A Semiparametric Quantile Single-Index Model for Zero-Inflated Outcomes”, Statistica Sinica, 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.

2023

• 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.

• 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.

• Ma, S. and Wang, T.✉ (2023). “The optimal pre-post allocation for randomized clinical trials”, BMC Medical Research Methodology, 23:72 doi: 10.1186/s12874-023-01893-w.

• 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.

2022

• 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., Ling, W., Plantinga, A., Wu, M., and Zhan, X. (2022). “Testing microbiome association using integrated quantile regression models”. Bioinformatics, 38(2), 419-425.

• Houghton, L. C., Wei, Y., Wang, T., Goldberg, M., Paniagua-Avila, A., Sweeden, R. L., Bradbury, A., Daly, M., Schwartz, L. A., Keegan, T., John, E. M., Knight, J. A., Andrulis, I. L., Buys, S. S., Frost, C. J., O’Toole, K., White, M. L., Chung, W. K., and Terry, M. B. (2022). “Body mass index rebound and pubertal timing in girls with and without a family history of breast cancer: the LEGACY girls study”. International Journal of Epidemiology, 2022 Feb 14:dyac021. doi: 10.1093/ije/dyac021. PMID: 35157067.

2021

• Wang, T.✉ and Asher, A. (2021). “Improved semiparametric analysis of polygenic gene-environment interactions in case-control studies”. Statistics in Biosciences, 13, 386–401.

• Plantinga, A., Wilson, N., Zheng, H., Wang, T., Zhan, Z., Wu, M., Zhao, N., and Chen, J. (2021). “MiRKAT: Microbiome Regression-Based Analysis Tests”.

2020 and before

• Gaynanova, I. and Wang, T. (2019). “Sparse quadratic classification rules via linear dimension reduction”. Journal of Multivariate Analysis, 169, 278-299.

• 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).

• Li, H., Staudenmayer, J., Wang, T., Keadle, S. K., and Carroll, R. J. (2018). “Three-part joint modeling methods for complex functional data mixed with zero-and-one–inflated proportions and zero‐inflated continuous outcomes with skewness”. Statistics in Medicine, 37(4), 611-626.

• Johnson, V., Payne, R., Wang, T., Asher, A., and Mandal, S. (2017). “On the reproducibility of psychological science”. Journal of the American Statistical Association, 112.517: 1-10.

• Wang, T., Yang, Y., and Tian, M. (2017). “Tuning Parameter Selection in Adaptive LASSO for Quantile Regression with Panel Data”. Journal of Applied Statistics and Management, 36(3): 429–440.

• Yin, J., Wang, T., and Wang, W. (2017). “Structure learning and parameter estimation on robust conditional graphical model”. China Science Paper, 12(17): 1921-1929.