Dr. Xiang Liming joined the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore as an assistant professor in 2007, and she is currently an Associate Professor of Statistics. She received her Ph.D. degree in statistics from the City University of Hong Kong in 2002, and was awarded the Outstanding Research Thesis Award for 2002-2003 by the City University of Hong Kong. She did her postdoctoral research at the Hong Kong University of Science and Technology and the City University of Hong Kong, respectively, during 2003-2006. Her research interests include survival analysis, longitudinal/clustered data analysis, mixture modelling and biostatistics. Dr. Xiang is an associate editor of Computational Statistics & Data Analysis and Statistics in Medicine.
Dr. Xiang Liming's areas of expertise are survival analysis, longitudinal/clustered data analysis, mixture modelling and biostatistics. Her current research work focuses on developing semiparametric methods for analysis of survival data subject to complex censoring that arises in health and biomedical studies.
- Extended mean residual life models for correlated survival data
- Yu, T., Xiang, L. and Wang, J.H. (2020). Quantile regression for survival data with covariates subject to detection limits. Biometrics, Accepted(1-12), https://doi.org/10.1111/biom.13309.
- Zsolt Szabo, Xiaoyu Liu and Liming Xiang. (2020). Semiparametric Sieve Maximum Likelihood Estimation for Accelerated Hazards Model with Interval-Censored Data. Journal of Statistical Planning and Inference, 205, 175-192.
- IL DO Ha, Liming Xiang, Mengjiao Peng, Jong-Hyeon Jeong and Youngjo Lee. (2020). Frailty Modelling Approaches for Semi-competing Risks Data. Lifetime Data Analysis, 26(1), 109-133.
- Rui Huang, Liming Xiang and Il Do Ha. (2019). Frailty Proportional Mean Residual Life Regression for Clustered Survival Data: A Hierarchical Quasi-likelihood Method. Statistics in Medicine, 38, 4854-4870.
- Mengjiao Peng, Liming Xiang, and Shanshan Wang. (2018). Semiparametric regression analysis of clustered survival data with semi-competing risks. Computational Statistics & Data Analysis, 124, 53-70.