[8] Zhou, Hongyi, Su Wenqing, Zhong Qixian* and Yang Ying* (2025+) Semiparametric Inference for Functional Survival Models, Statistica Sinica, forthcoming.

[7] Wang, Jane-Ling* and Zhong, Qixian* (2025) Joint modeling of longitudinal and survival data, Annual Review of Statistics and Its Application, 12, 449-476.

[6] Hao, Siteng#, Lin, Shu-Chin.#, Wang, Jane-Ling#, and Zhong, Qixian# (2024). Dynamic modeling for multivariate functional and longitudinal data, Journal of Econometrics, 239(2): 105573.

[5] Zhong, Qixian, and Wang, Jane-Ling (2024). Neural networks for partially linear quantile regression. Journal of Business & Economic Statistics, 42(2): 603-614.

[4] Zhong, Qixian, Mueller, Jonas, and Wang, Jane-Ling (2022). Deep learning for partially linear Cox model. The Annals of Statistics. 50(3): 1348-1375.

[3] Zhong, Qixian, Mueller, Jonas, and Wang, Jane-Ling (2021). Deep extended hazard models for survival analysis. Advances in Neural Information Processing Systems 34 (NeurIPS 2021). 15111-15124.

[2] Lin, Zhenhua#, Wang, Jane-Ling#, and Zhong, Qixian# (2021). Basis expansions for functional snippets. Biometrika. 108(3): 709-726.

[1] Zhang, Xiaoke, Zhong, Qixian, and Wang, Jane-Ling (2020). A new approach to varying coefficient additive model with longitudinal data. Computational Statistics & Data Analysis. 145.

#Authors contributed equally, *Corresponding author