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