Publications

My Google Scholar

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

• 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 and Overdispersed Outcomes”, Statistica Sinica, accepted.
  • Won the Best Student Paper Award in the 2024 National Graduate Statistics Symposium, Section of Mathematical Statistics.

• 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.✉, 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, 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.

• 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.

• 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.

• 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.

• 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.

• 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”.

• 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.

• Delaigle, A., Hyndman, T., and Wang, T. “deconvolve-package: Deconvolution Tools for Measurement Error Problems.”

Under Review

• Wang, T.✉, Ionita-Laza, I., and Wei, Y. (2023+). “A unified quantile framework reveals nonlinear heterogeneous transcriptome-wide associations”.

• Zhao, H. and Wang, T.✉ (2023+). “A pseudo-simulation extrapolation method for misspecified models with errors-in-variables in epidemiological studies”.

• Wang, T.✉, Ma, Y. and Wei, Y. (2024+) “Joint Quantile Regression with Latent Clustering for Age-Dependent Genetic Effects on Multiple Traits”.

• Liu, Y. and Wang, T.✉ (2024+). “A powerful transformation of quantitative responses for biobank-scale association studies”.

• Li, Y., Wang, T.✉, Yan, J., and Zhang, X. (2024+). “Detection and Attribution Analysis of Regional Temperature with Estimating Equations”.

• Mao, Y., Jiang, Z., Wang, T., and Zhan, X. (2024+) “Tree-guided compositional variable selection analysis of microbiome data”.