Under Review

• Murali, N., Lee, A., Senathirajah, L., Otify, E., Zhou, J., Ali, T., & Stather, P. (2025+). Concurrent Peripheral Arterial Disease Predicts Re-Intervention But Not Mortality Following AAA Repair: A Single-Center Propensity Matched Retrospective Study. Submitted to European Journal of Vascular & Endovascular Surgery.

• Zhou, J. & Zou, H. (2025+). High-dimensional Newey-Powell Test via Approximate Message Passing. Econometric Theory revision submitted, arXiv:2311.05056.

• He, D.J., Xu, S.R. & Zhou, J. (2025+). A Bayesian Two-Sample Mean Test for High-Dimensional Data. Submitted to Econometrics and Statistics.

Working paper

• Zhou, J. & Li, C.L. (2024+). Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution. In preparation for submission to Transactions on Machine Learning Research, arXiv:2412.05759.

• Chen, G.P., Li, C.L. & Zhou, J. (2025+). Deep Instrumental Variable Framework under Unconditional Quantile Regression (DeepUQR). In preparation for submission to ICML 2026.

• Zhou, J. & Zhang, Z. (2025+). High dimensional quasi-likelihood under covariance matrix misspecification. In preparation.

• Zhou, J., Venkataramanan, R., & Zou, H. (2025) Modern asymptotic theory for heteroscedastic regression. In preparation.

Journal articles

1. Zhou, J. & Scherr, S (2025). Model-X knockoffs in the replication crisis era: Reducing false discoveries and researcher bias in social science research. Social Sciences & Humanities Open. DOI:10.1016/j.ssaho.2025.101380.

2. Zhou, J. & Claeskens, G. (2024). Sample size calibration by FDR-power tradeoff for logistic regression in high dimensions. Electronic Journal of Statistics.

3. Claeskens, G., Janssen, M. & Zhou, J. (2023). Discussion on: “A Scale-free approach for false discovery rate control in generalized linear models” by Dai, Lin, Zing, Liu. Journal of the American Statistical Association, 118(543), 1573–1577. [Link]

4. Zhou, J. & Claeskens, G. (2022). Automatic bias correction for testing in high dimensional linear model. Statistica Neerlandica, 77(1), 71-98. [Link]

5. Tepegjozova M., Zhou, J., Claeskens G. & Czado, C. (2022). Nonparametric C- and D-vine based quantile regression. Dependence Modeling, 10(1), 1-21. [Link]

6. Zhou, J., Claeskens, G. & Bradic, J. (2020). Detangling robustness in high-dimensions: composite versus model-averaged estimation. Electronic Journal of Statistics, 14(2), 2551-2599. [Link]

7. Bloznelis D., Claeskens G. & Zhou J. (2019) Composite versus model-averaged quantile regression. Journal of Statistical Planning and Inference, 200, 32-46. [Link]

8. Scherr, S & Zhou, J. (2019). Automatically Identifying Relevant Variables for Linear Regression with the Lasso Method: A Methodological Primer for its Application with R and a Performance Contrast Simulation with Alternative Selection Strategies. Communication Methods and Measures, DOI: 10.1080/19312458.2019.1677882. [Link]

Proceedings

• Scherr, S. & Zhou, J. (2024). How to address false discovery bias in big data using model-X knockoffs for variable selection. The 107th AEJMC Conference, Philadelphia, PA, August 8-11, 2024.

• Zhou, J., Claeskens, G. & Bloznelis, D. (2018). Weight choice for penalized composite quantile regression and for model averaging. Proceedings of the 33rd International Workshop on Statistical Modelling, University of Bristol, UK, July 16-20, 2018. Pages 219-224.

Thesis

• Zhou, J. (2020). High dimensional quantile regression: composite estimation and model averaging. [Link]