期刊论文(*代表通讯作者):
27. Ma, H., Cheng, M., Liu, Y., Zeng, D., and Zhou, Y. (2025). Efficient estimation of the accelerated failure time model with auxiliary aggregate information. Statistica Sinica. Accepted.
26. Ma, H., Zhao, W., Hanfelt, J., and Peng, L.(2025). Dynamic regression of longitudinal trajectory features. Journal of the American Statistical Association. Accepted.
25. Gu, Z., Liu, S., Ma, H., Long, Y., Jiao, X., Gao, X., Du, B., Bi, X., and Shi, X. (2025). Estimation of machine learning–based models to predict dementia risk in patients with atherosclerotic cardiovascular diseases: UK biobank study. JMIR Aging. Accepted.
24. Li W., Ma, H.* and Zhou Y. (2025). Mean residual life causal models for survival data with a binary instrumental variable. Communications in Mathematics and Statistics. Accepted.
23. Zhao Z., Ma, H.*, and Zhou Y.* (2025). Additive hazard causal model with a binary instrumental variable. Statistical Methods in Medical Research. Accepted.
22. Cheng, M., Liu, Y., Ma, H.*, and Qin, J. (2024). Maximum full likelihood approach to randomly truncated data. Journal of Systems Science & Complexity. https://doi.org/10.1007/s11424-024-3288-8.
21. Ma, H., Qin, J. and Zhou, Y. (2023). From conditional quantile regression to marginal quantile estimation with applications in missing data and causal inference. Journal of Business & Economic Statistics. 41(4): 1377-1390.
20. Li, W., Ma, H.*, Faraggi, D, and Dinse, G. (2023). Generalized mean residual life models for survival data with missing censoring indicators. Statistics in Medicine. 42(3): 264-280.
19. Ma, H.*, Qin, J., Chen, F., and Zhou, Y. (2023). A novel nonparametric mixture model for the infection pattern of COVID-19 on Diamond Princess cruise. Statistical Theory and Related Fields. 7:1, 85-96.
18. Ma, H., Zheng, Q., Zhang, Z., Lai, H-C., and Peng, L. (2023). Globally adaptive longitudinal quantile regression with high dimensional compositional covariates. Statistica Sinica. 33, 1295-1318.
17. Ma, H.*, Pang, W., Sun, L., and Xu, W. (2022). Augmented weighting estimators for the additive rates model under multivariate recurrent event data with missing event type. Statistics in Medicine. 41(22): 4285–4298.
16. Qiu, Z., Ma, H.*, and Shi, J. (2022). Reweighting estimators for the transformation models with length-biased sampling data and missing covariates, Communications in Statistics-Theory and Methods, 51(13): 4252--4275.
15. Qiu, Z., Ma, H.*, Chen, J., and Dinse, G. (2021). Quantile regression models for survival data with missing censoring indicators. Statistical Methods in Medical Research. 30(5): 1320–1331.
14. Ma, H., Peng, L., Huang, C-Y. and Fu, H. (2021). Heterogeneous individual risk modelling of recurrent events. Biometrika. 108(1): 183–198.
13. Ma, H., Zhao, W. and Zhou, Y. (2020). Semiparametric model of mean residual life with biased sampling data. Computational Statistics and Data Analysis. 142: 106826.
12. Ma, H., Peng, L. and Fu, H. (2019). Quantile regression modeling of latent trajectory features with longitudinal data. Journal of Applied Statistics. 46: 2884-2904.
11. Ma, H., Shi, J. and Zhou, Y. (2019). Proportional mean residual life model with censored survival data under case-cohort design. Statistics and Its Interface. 12: 21-33.
10. Ma, H., Peng, L., Zhang, Z. and Lai, H-C. (2018). Generalized accelerated recurrence time models for multivariate recurrent events data with missing event type. Biometrics. 74: 954-965.
9. Fan, C., Ma, H.* and Zhou, Y. (2018). Quantile regression for competing risks analysis under case-cohort design. Journal of Statistical Computation and Simulation. 88: 1060-1080.
8. Shi, J., Ma, H. and Zhou, Y. (2018). The nonparametric quantile estimation for length-biased and right censored data. Statistics and Probability Letters. 134: 150-158.
7. Li, Y., Ma, H., Wang, D. and Zhou, Y. (2017). Analyzing the general biased data by additive risk model. Science China Mathematics. 60: 685-700.
6. Ma, H.* and Zhou, Y. (2017). Pseudo likelihood for case-cohort studies under length-biased sampling. Communications in Statistics-Theory and Methods. 46: 28-48.
5. Ma, H.*, Qiu, Z. and Zhou, Y. (2016). Semiparametric analysis of transformation models with length-biased data under case-cohort design. Statistics and Its Interface. 9: 213-222.
4. Tian, G., Ma, H.*, Zhou, Y. and Deng, D. (2015). Generalised endpoint-inflated binomial model. Computational Statistics and Data Analysis. 89: 97-114.
3. Ma, H.*, Zhang, F. and Zhou, Y. (2015). Composite estimating equation approach for the additive risk model with length-biased and right-censored data. Statistics and Probability Letters, 96: 45-53.
2.马慧娟, 范彩云和周勇(2015).长度偏差右删失数据下分位数回归的估计方程方法. 中国科学：数学. 45:1981-2000.
1.肖鸿民, 马慧娟(2011).负相依重尾索赔条件下损失过程的精细大偏差. 兰州大学学报（自然科学版）. 3: 101-146.
摘要：
4. Zhang, Z., Ma, H., Peng, L., Lai, H-C., and the FIRST Study Group. (2018). Different lung disease presentations in infants with Cystic Fibrosis. Pediatric Pulmonology. 53(S2): S329-S329.
3. Ma, H., Peng, L., Zhang, Z., Lai, H-C., and the FIRST Study Group. (2017). Investigating the dynamic heterogeneity of weight growth in infants with Cystic Fibrosis through a novel statistical analysis. Pediatric Pulmonology. 52(S47): S408-S409.
2. Yang, J., Peng, L., Zhang, Z., Rahman, F, Ma, H., Lai, H-C, and the FIRST Study Group. (2016). Joint profile of respiratory infections and their association with breastfeeding in infants with Cystic Fibrosis. Pediatric Pulmonology. 51(S45): S327-S327.
1. Zhang, Z., Ma, H., Peng, L, Lai, H-C, and the FIRST Study Group.(2016). Gut microbiota in early childhood in Cystic Fibrosis. Pediatric Pulmonology. 51(S45): S325-S325.
