Probability & Statistics
Faculty with research interests in this area:
- Dr. Xin Dang
- Dr. Hailin Sang
- Dr. Martial Longla
- Dr. Dao Nguyen
- Dr. Jeremy Clark
This group of faculty works on problems in various fields of statistics and probability including but not limited to the following:
- Mathematical statistics
- Applied statistics
- Bayesian statistics
- Nonparametric statistics
- Robust statistics
- Markov chains
- Stochastic processes
- Time series
- Machine learning theory and application
- Directed polymer in a random environment
- Invariance principles
More specifically, questions of interest include nonparametric and robust multivariate analysis, data mining, outlier identification, cluster analysis, applications of depth functions, analysis of dependent data, copula applications in estimation theory, theory of copulas, inference for time series, random fields, properties of self-normalized statistics, empirical processes and applications, deep learning models and theory, moderate or large deviations, survey sampling design and analysis, Markov and reversible Markov chains (limit theorems and copula approach), central limit theorems for dependent data, dependence modelling and applications, kernel estimation methods for dependent data, Bayesian analysis, survival analysis, large sample theory. These questions include their applications to various fields of research for interdisciplinary collaboration.