Articles
[1] Zhang, N., Liu, P.*, Kong, L., Jiang, B. and Huang, J. (2023). Functional Linear Quantile Regression on a Two-dimensional Domain. Bernoulli. Accepted.
[2] Liu, P.*, Huang, Y., Chan, KCG. and Chen, Y. (2023). Semiparametric Trend Analysis for Stratified Recurrent Gap Times under Weak Comparability Constraint. Statistics in Biosciences 15: 455-474.
[3] Ren, S.+, Wang, X., Liu, P. and Zhang, J. (2023). Bayesian Nonparametric Mixtures of Exponential Random Graph Models for Ensembles of Networks. Social Networks 74: 156-165.
[4] Liu, P.*, Chan, K. and Chen, Y. (2023). On a Simple Estimation of the Proportional Odds Model under Right Truncation. Lifetime Data Analysis 29: 537-554.
[5] Liu, M., Pietrosanu, M., Liu, P., Jiang, B., Zhou, X. and Kong, L. (2022). Reproducing kernel-based functional linear expectile regression. The Canadian Journal of Statistics 50: 241-266.
[6] Liu, P., Song, S. and Zhou, Y. (2022). Semiparametric Additive Frailty Hazard Model for Clustered Failure Time Data. The Canadian Journal of Statistics 50: 549-571.
[7] Cheng, M., Huang, T., Liu, P. and Peng, H. (2018). Bias Reduction for Nonparametric and Semiparametric Regression Models. Statistica Sinica 28: 2749-2770. [Special Issue In Memory of Peter G. Hall]
[8] ZHANG, L., Liu, P. and ZHOU, Y. (2015). Smoothed Estimator of Quantile Residual Lifetime for Right Censored Data. Journal of Systems Science and Complexity 28: 1374–1388.
[9] Liu, P., Wang, Y. and Zhou, Y. (2014). Quantile residual lifetime with right-censored and length-biased data. Annals of the Institute of Statistical Mathematics 67: 999-1028.
[10] Wang, Y., Liu, P. and Zhou, Y. (2014). Quantile residual lifetime for left-truncated and right-censored data. Science in China Series A: Mathematics 58: 1217–1234.
[11] Liu, Y., Liu, P. and Zhou, Y. (2014). Smoothing Nonparametric Estimator of Quantile Residual Lifetime under Compete Risk (in Chinese). Acta Mathematicae Applicatae Sinica (Chinese Series) 38:109-124.
Conference or workshop item
[1] Liu, P.*, Zhu, R., Liu, Y., Kong, L., Jiang, B., and Niu, D. (2023). Quantile Matrix Factorization: an Optimal Algorithm via Smooth Minimization. SIAM International Conference on Data Mining (SDM 2023). Accepted. (Top conference in Data Mining)
[2] Wang, Y., Pan, B., Tu, W., Liu, P., Jiang, B., Gao, C., Lu, W., Jui, S., and Kong, L. (2022). Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability. In: 36th AAAI Conference on Artificial Intelligence (AAAI-22). (Acceptance rate: 15%, A* conference in CORE2020 ranking, top conference in Artificial Intelligence)
[3] Tu, W., Liu, P., Liu, Y., Li, G., Jiang, B., Kong, L., Yao, H., and Jiu, S. (2021). Nonsmooth Low-rank Matrix Recovery: Methodology, Theory and Algorithm. In: Future Technologies Conference (FTC) 2021.
[4] Liu, P., Tu, W., Zhao, J., Liu, Y., Kong, L., Li, G., Jiang, B., Tian, G. and Yao, H. (2020). M-estimation in Low-rank Matrix Factorization: a General Framework. In: 19th IEEE International Conference on Data Mining. IEEE, pp. 568-577. (Acceptance rate: 9.08%, A* conference in CORE2020 ranking)
[5] Hu, Y., Liu, P., Ge, K., Kong, L., Jiang, B. and Niu D. (2020). Learning Privately over Distributed Features: An ADMM Sharing Approach. In: NeurIPS-20 Workshop on Scalability, Privacy, and Security in Federated Learning. (A* conference in CORE2020 ranking, top conference in Computer Science)
Forthcoming
[1] Wang, Y., Pan, B., Tu, W., Liu, P., Jiang, B., Lu, W., Jui, S., and Kong, L. (2021). Convergence Analysis of the Stochastic Krasnosel’skii-Mann Algorithm. Submitted.
[2] Liu, P.*, Huang, Y., Chan, K., and Chen, Y. (2020). Semiparametric Trend Analysis for Stratified Recurrent Gap Times under Weak Comparability Constraint. Submitted.
[3] Liu, P.* (2020). Statistical Inference on the Monotonic Index Model Based on Monotone Rank Estimate. Submitted.
*: Corresponding author. +: Student supervised