Research

Research interest:

• Extreme value theory

• Statistical inference for big streaming data

• Bootstrap and related area

• High-Dimensional data analysis

• Machine learning

Refereed journal papers:        * Corresponding author 

1. Baček, T., Xu, Y., Peng, L., Oetomo, D. and Tan, Y. (2025). Gait adaptations in step length and push-off force during walking with functional asymmetry, Frontiers in Bioengineering and Biotechnology, 13, 1550710.

2. Huang, W., Li, S. and Peng, L. (2024). Estimation and Inference for Extreme Continuous Treatment Effects, Journal of Business & Economic Statistics, https://doi.org/10.1080/07350015.2024.2430293.

3. Cheng, G., Peng, L. and Zou, C. (2024+). Statistical Inference for Ultrahigh Dimensional Location Parameter Based on Spatial  Median, Statistica Sinica, accepted, https://doi.org/10.5705/ss.202023.0242.

4. Cheng, G., Lin, R. and Peng, L. (2024). High-dimensional Multivariate Analysis of Variance via Geometric Median and Bootstrapping, Biometrics, 80(3). https://doi.org/10.1093/biomtc/ujae088

5. Cofré Lizama, L. E., Panisset, M. G., Peng, L., Tan, Y., Kalinicik, T. and Galea, M. P. (2024). Postural behaviour in people with multiple sclerosis: A complexity paradox, Gait & Posture, 111, 14-21.

6. Cofré Lizama, L. E., Panisset, M. G., Peng, L., Tan, Y., Kalinicik, T. and Galea, M. P. (2023). Optimal sensor location and direction to accurately classify people with early-stage multiple sclerosis using gait stability, Gait & Posture, 102, 39-42.

7. Li, S., *Peng, L. and Song, X. (2023). Simultaneous Confidence Bands for Conditional Value-at-Risk and Expected Shortfall, Econometric Theory, 39(5), 1009-1043. doi.org/10.1017/S0266466622000275

8. Peng, L., Wang, G. and Zou, C. (2023). Measuring, Testing and Identifying Heterogeneity of Large Parallel Datasets, Statistica Sinica, 33, 2787-2808. doi:10.5705/ss.202021.0285. 

9. Huang, B., Liu, Y. and *Peng, L. (2023). Weighted bootstrap for two-sample U-statistics, Journal of Statistical Planning and Inference, 226, 86-99. https://doi.org/10.1016/j.jspi.2023.02.004.

10. Huang, B., Liu, Y. and *Peng, L. (2023). Distributed inference for two-sample U-statistics in massive data analysis, Scandinavian Journal of Statistics, 50(3), 1090-1115. https://doi.org/10.1111/sjos.12620.

11. **Wang, Z., **Peng, L. and Kim, J. K. (2022). Bootstrap Inference for the Finite Population Total Under Complex Sampling Designs, Journal of the Royal Statistical Society: Series B, 84(4), 1150-1174. doi.org/10.1111/rssb.12506. (** Co-first authors)

12. Li, S., *Peng, L. and Tu, Y. (2022). Testing Independence Between Exogenous Variables and Unobserved Errors, Econometric Reviews, 41(7), 697-728.

13. Mao, X., Peng, L. and Wang, Z. (2022). Nonparametric Feature Selection by Random Forests and Deep Neural Networks, Computational Statistics and Data Analysis, 170, 107436.

14. Cheng, G., Liu, Z. and Peng, L. (2022). Gaussian approximations for high-dimensional non-degenerate U-statistics via exchangeable pairs, Statistics & Probability Letters, 182, 109295.

15. Xia, Y., Mohammadi, A., Peng, L., Tan, Y., Chen, B., Choong, P. and Oetomo, D. (2022). Beta Mixture Model for the Uncertainties in Robotic Haptic Object Identification, IEEE Transactions on Mechatronics, 22(4), 1955-1963. doi: 10.1109/TMECH.2022.3175491.

16. Chen, S. X. and Peng, L. (2021). Distributed Statistical Inference for Massive Data, Ann. Statist., 49(5), 2851-2869.

17. *Peng, L., Qu, L. and Nettleton, D. (2021). Variable Importance Assessments and Backward Variable Selection for Multi-Sample Problems, Journal of Multivariate Analysis, 186, 104807.

18. Cheng, G., Liu, B., Peng, L., Zhang, B. and Zheng, S. (2019). Test Equality of Two High Dimensional Spatial Sign Covariance Matrices, Scandinavian Journal of Statistics, 46, 257-271.

19. *Peng, L., Chen, S. X. and Zhou, W. (2016). More Powerful Tests for Sparse High-Dimensional Covariance Matrices, Journal of Multivariate Analysis, 149, 124-143.

20. Zou, C., Peng, L., Feng, L. and Wang, Z. (2014). Multivariate Signs-based High-Dimensional Tests for Sphericity, Biometrika, 101(1), 229-236.

Refereed conference papers:

1. Tian, X., Peng, L., Zhou, Z., Gong, M., Gretton, A. and Liu, F. (2025). A Unified Data Representation Learning for Non-parametric Two-sample Testing, UAI.

2. Hu, G., Liu, F., Gong, M, Wang, G. and *Peng, L. (2025). Learning Imbalanced Data with Beneficial Label Noise, ICML.

3. Li, Y., Xia, Y., Wang, X., Chen, Z., Peng, L., Gong, M. and Zhang, K. (2025). Extracting Rare Dependence Patterns via Adaptive Sample Reweighting, ICML.

4. Du, W., Min, Y., Li, J., Lu, K., Zou, C., Peng, L., Chu, T. and Gong, M. (2025). LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning, ICLR.

5. Wang, X., Huo, Y., *Peng, L. and *Zou, C. (2024). Conformalized multiple testing after data-dependent selection, NeurIPS.

6. Hu, D., Fu, H., Guo, J., Peng, L., Chu, T., Liu, F., Liu, T. and Gong, M. (2024). In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment, NeurlPS.

7. Hu, D., Peng, L., Chu. T., Zhang, X., Mao, Y., Bondell, H. and Gong, M. (2022). Uncertainty Quantification in Depth Estimation via Constrained Ordinal Regression. ECCV.