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.