Under Review

• Zhou, J. & Scherr, S. (2023). APIcalypse Now? How to Avoid False Discovery Bias in Big Data Using the ‘Knockoff Method’ for Variable Selection. Submitted to Communication Methods and Measures.

• Zhou, J. & Zou, H. (2023). High dimensional Newey-Powell test via Approximate Message Passing. Submitted to Journal of Econometrics.

Journal articles

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

2. 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]

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

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

5. 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]

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

7. 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

• 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]