Fundamental Research in Statistics
Fundamental research in statistics includes developing new theory to validate statistical procedures, generalizing probability models for random processes, developing new nonparametric methodology for machine learning applications, establishing the asymptotic theory behind new statistical methods and proving new approaches for experiments that cannot be handled by traditional statistical methods. Areas of interest among our faculty include:
- Nonparametric and semiparametric statistics
- Bayesian methods and machine learning
- Inferential methods for dependent and longitudinal data
- High dimensional data analysis and model selection
- Monte Carlo methods in statistical computing
- Time series and spatial-temporal models
Faculty working in Fundamental Research in Statistics
- Undergraduate students in the Department of Statistics presented research projects to peers, mentors, and general attendees at the Fall 2021 Undergraduate Research Experience in Statistics (URES)...
- Department of Statistics seeks Instructor, Lecturer, Teaching Track Professors, and Clinical Track ProfessorsThe Department of Statistics invites applications for instructors, lecturers, teaching track assistant/associate/full professors and clinical track assistant/associate/full professors. Some areas of...