Ruoyu Wang

Ruoyu Wang

Postdoc in Biostatistics

Publications

Peer-Reviewed Publications

  1. Wang, R., Wang Q.*, and Miao, W. (2023), A robust fusion-extraction procedure with summary statistics in the presence of biased sources. Biometrika, 110, 1023–1040.
  2. Wang, R., Su, M., and Wang, Q.* (2023), Distributed nonparametric imputation for missing response problems with massive data. Journal of Machine Learning Research, 68, 1–52.
  3. Wang, R., and Wang, Q.* (2021), Determination and estimation of optimal quarantine duration for infectious diseases with application to data analysis of COVID-19. Biometrics, 78, 691–700.
  4. Wang, R., Wang, Q.*, Miao, W., and Zhou, X. (2024), Sharp bounds for variance of treatment effect estimators in the finite population in the presence of covariates. Statistica Sinica, 34, 999–1021.
  5. Su, M. and Wang, R.* (2025+), Subsampled one-step estimation for fast statistical inference. Scandinavian Journal of Statistics, in press.
  6. Wang, R.1, Yi, M.1, Chen, Z., and Zhu, S. (2022), Out-of-distribution generalization with causal invariant transformations. IEEE Conference on Computer Vision and Pattern Recognition, 375–385.
  7. Yi, M.1, Wang, R.1, and Ma, Z. (2022), Characterization of excess risk for locally strongly convex population risk. Advances in Neural Information Processing Systems 36.
  8. Yi, M., Wang, R., Sun, J., Li, Z., and Ma, Z. (2023), Breaking correlation shift via conditional invariant regularizer. In Proceedings of the 11th International Conference on Learning Representations.
  9. Yang, H., Liu, Z., Wang, R., Lai, E., Schwartz, J., Baccarelli, A., Huang, Y. and Lin, X.* (2025), Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data. Annual Review of Statistics and Its Application, 12, 337–360.
  10. Su, M.1, Wang, R.1, and Wang, Q.* (2022), A two-stage optimal subsampling estimation for missing data problems with large-scale data. Computational Statistics and Data Analysis, 173.
  11. Wang, Q., Su, M.*, and Wang, R. (2021), A beyond multiple robust approach for missing response problem. Computational Statistics and Data Analysis, 155.
  12. Miao, W.*, Li, W., Hu, W., Wang, R., and Geng, Z. (2021), Invited commentary: Estimation and bounds under data fusion. American Journal of Epidemiology, 191, 674–678.

Paper under Invited Revision

  1. Wang, R., Zhang, H., and Lin X.* (2025+), Debiased estimating equation method for versatile and efficient Mendelian randomization using a large number of correlated weak and invalid instruments. Revision invited by Journal of the American Statistical Association: T&M. arXiv:2408.05386.
  2. Hu, W.1, Wang, R.1, Li, W.*, and Miao, W.* (2025+), Semiparametric efficient fusion of individual data and summary statistics. Revision invited by Journal of the American Statistical Association: T&M. arXiv:2210.00200.
  3. Wang, R. and Lin X.* (2025+), Divide-and-shrink: An efficient and heterogeneity-agnostic approach for transfer estimation using summary statistics. Revision invited by Journal of the Royal Statistical Society: Series B.
  4. Su, M. and Wang, R.* (2025+), A moment-assisted approach for improving subsampling-based MLE with large-scale data. Revision invited by Journal of Machine Learning Research. arXiv:2309.09872.
  5. Yang, H.1, Wang, R.1, Lin, Y., and Lin, X.* (2025+), Tail likelihood ratio method for large-scale causal mediation testing in epigenome-wide studies. Revision invited by Journal of the American Statistical Association: ACS.
  6. Wang, R., Wang Q.*, and Miao, W. (2025+), A maximin optimal approach for sampling designs in two-phase studies. Revision invited by Statistica Sinica. arXiv:2312.10596.

Paper under Review

  1. Wang, R. and Miao, W.* (2025+), Causal Effect Identification and Inference with Endogenous Exposures and a Light-tailed Error. Under review. arXiv:2408.06211.
  2. Su, M. and Wang, R.* (2025+), Moment-assisted subsampling method for Cox proportional hazards model with large-scale data. Under review. arXiv:2501.06924.
  3. Zhang, P., Wang, R., and Miao, W.* (2025+), Causal attribution with confidence. Under review. arXiv:2504.08294.

Preprint

  1. Yi, M., Matabuena, M., Wang, R.* (2025+), Denoising data with measurement error using a reproducing kernel-based diffusion model. arXiv:2501.00212.

1 : equal contribution; * : corresponding author

Oral Presentation

  1. Characterization of excess risk for locally strongly convex population risk. Chinese Association for Applied Statistics (CAAS), High Dimensional Statistics Symposium, July 2021.
  2. Sharp bounds for variance of the treatment effect estimators in finite population in the presence of covariates. The 2021 International Workshop on Statistical Theory and Related Fields (STARF 2021), December 2021.
  3. Debiased estimating equation method for summary statistics-based Mendelian randomization. The 1st Joint Conference on Statistics and Data Science in China, July 2023.
  4. DEEM: A flexible and efficient method for summary statistics-based Mendelian randomization. ENAR 2024 Spring Meeting, March 2024.
  5. Divide-and-shrink: a heterogeneity-agnostic approach for safe data integration. Invited talk, IMS-China 2024, July 2024.
  6. Extreme-based causal effect learning with endogenous exposures and a light-tailed error. Invited talk, 2024 International Conference on Frontiers of Data Science, July 2024.
  7. Debiased estimating equation method for versatile and efficient Mendelian randomization using large numbers of correlated weak and invalid instruments. The 2nd Joint Conference on Statistics and Data Science in China, July 2024.
  8. Debiased estimating equation method for versatile and efficient Mendelian randomization. 2024 Joint Statistical Meetings (JSM), August 2024.
  9. Extreme-based causal effect learning with endogenous exposures and a light-tailed error. Seminar of the Center for Causal Inference, University of Pennsylvania, March 2025.
  10. Divide-and-shrink: An efficient and heterogeneity-agnostic approach for transfer estimation using summary statistics. Invited talk, Diabetes StatClin Meeting, April 2025.