The Statistics Group is a large and diversified research group with interests in survey sampling, data mining, classification, pattern recognition, nonparametric methods, curve estimation, bootstrap methods, recurrent event data, models for event processes, estimation, order restricted and robust inference, dependent data analysis, biostatistics, longitudinal data analysis, missing data analysis, order statistics and empirical Bayes estimation.


Research Interests

Ahmed Almaskut Applied Statistics and Education Statistics
Miklos Csorgo Asymptotic Laws and Methods in Probability and Statistics, Stochastic Processes, Random Walk Models, Weighted Approximations/Invariance Principles, Short and Long Memory Stochastics, Change-Point Analysis, Re-sampling Methods with Applications to Infinite Super-Populations and Big Data Sets
Song Cai Empirical Likelihood, Asymptotic Theory, Density Ratio Model, Non-and Semi-parametric Inference, Statistical Computing
Dave Campbell Bayesian modeling, MCMC, Dynamic Systems, Machine Learning, Text and Image data, Functional Data Analysis, Data Science for Environmental and Social Good
Patrick Farrell Categorical Data Analysis, Biostatistics, Sampling, Applied Statistics
Shirley Mills Data Mining, Applied Statistics
Jason Nielsen Functional Data Analysis, Longitudinal Data Analysis, Mixture Models, Computational Statistics/Numerical Analysis
Mohamedou Ould Haye Stochastic Processes, Time Series Analysis, Limit Theorems, Estimation, Long Memory, Forecasting, Dependence
J.N.K. Rao Survey Sampling, Statistical Inference
Sanjoy Singh Biostatistics, Longitudinal Data Analysis, Missing Data Analysis, Mixed Models and Robust Inference
Natalia Stepanova Nonparametric Estimation, Nonparametric Hypothesis Testing
Paul Villeneuve Biostatistics, Methods in Longitudinal Data, Multivariable Regression, Analysis of Administrative Health Datasets, Record Linkage Studies, Demography, and Epidemiology