Haiming Zhou, Ph.D.
Assistant Professor, Director of Statistical Consulting Services
Haiming Zhou earned a Ph.D. in statistics from the University of South Carolina, an M.S. in mathematical sciences from Clemson University and a B.S. degree in statistics from the University of Science and Technology of China.
Haiming's eclectic research interests include survival analysis, Bayesian nonparametrics, variational Bayesian methods, measurement error models, frequentist nonparametric methods, semiparametric regression, spatial analysis, copulas, statistical computing for large datasets, mode regression, group testing data analysis, causal mediation analysis, and applications in epidemiology/public health.
His recent projects involve density regression with measurement error, mode regression for bounded data, Bayesian model selection, spatial copulas, continuously stratified survival models, and R software package development for big survival data.
Zhou, H., and Hanson, T. (2018). A unified framework for fitting Bayesian semiparametric models to arbitrarily censored survival data, including spatially-referenced data. Journal of the American Statistical Association, 113(522): 571-581.
Zhou, H., and Huang, X. (2016). Nonparametric modal regression in the presence of measurement error. Electronic Journal of Statistics, 10(2): 3579-3620.
Zhou, H., Hanson, T., and Knapp, R. (2015). Marginal Bayesian nonparametric model for time to disease arrival of threatened amphibian populations. Biometrics, 71(4): 1101-1110.
Call for Papers - A Special Issue of Stats
Survival analysis has a broad range of applications in fields that deal with time-to-event data, such as public health, engineering, biomedical science, actuarial science, and environmental science. This Special Issue will present a collection of the latest developments in survival models and their applications to new subject-matter challenges. Suitable topics include, but are not limited to, flexible but interpretable regression models, Bayesian survival models, spatial survival models, competing risk models, cure rate models, discrete survival models, methods for analyzing data in non-standard settings, and software development. Manuscripts that apply state-of-the-art survival models to new and ongoing real-world problems (e.g., the COVID-19 epidemic) are especially welcome. See the Special Issue website for manuscript submission information.