Huijuan Ma

Reviews for journals Biometrics, American Journal of Epidemiology, Computational Statistics and Data Analysis, Lifetime Data Analysis, Journal of Nonparametric Statistics, Journal of Applied Statistics, Statistics and Its interface etc.

Survival Analysis;

Quantile Regression;

Causal Inference;

Big Data Analysis;

1. Undergraduate Course <Statistical Software> 

2. Graduate Courses <Statistical Software and Computing>, <Academic Writing and Latex>,

             <Statistical Analysis of Complicated Data>

1. National Natual Science Foundation of China，"Semiparametric regression models and their applications for

multivariate recurrent event data with missing event type", 240, 000 Yuan, Principle Investigator 

2. Shanghai Pujiang Program, "Quantile regression and its applications of latent trajectory features for longitudinal data", 300, 000 Yuan, Principle Investigator

Papers in English (* represents corresponding author):

20. Cheng, M., Liu, Y., Ma, H.*, and Qin, J. (2023+). Maximum full likelihood approach to randomly truncated data. Journal of Systems Science & Complexity. Accepted.

19. 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.

18. 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. 

17. 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.

16. 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.  

15. 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.

14. 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.

13. Qiu, Z., Ma, H.*, Chen, J., and Dinse, G. (2021). Quantile regression for survival data with missing censoring indicator.Statistical Methods in Medical Research.  30(5): 1320–1331.

12.  Ma, H., Peng, L., Huang, C-Y. and Fu, H. (2021). Heterogeneous individual risk modelling for recurrent events.  Biometrika. 108(1): 183–198. 

11. 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. 

10. 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.

9. 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.

8. 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.

7. 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. 

6. 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.

5. 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.

4. 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.

3. 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.

2.  Tian, G., Ma, H.*, Zhou, Y. and Deng, D. (2015). Generalised endpoint-inflated binomial model.  Computational Statistics and Data Analysis. 89: 97-114. 

1. 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.

Abstracts:

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.