statistics

The Financial Econometrics Lab is interested in statistical methods in financial econometrics and risk managements, computational biology, biostatistics, high-dimensional statistical learning, data-analytic modeling, longitudinal and functional data analysis, nonlinear time series, wavelets and their applications, among others. Our primary research focuses on developing and justifying statistical methods that are used to solve problems from the frontiers of scientific research. This is expanded into other disciplines where the statistics discipline is useful.

This group is interested in statistical methods in financial econometrics and risk managements, computational biology, biostatistics, high-dimensional statistical learning, data-analytic modeling, longitudinal and functional data analysis, nonlinear time series, wavelets and their applications, among others. The primary research focuses on developing and justifying statistical methods that are used to solve problems from the frontiers of scientific research. This is expanded into other disciplines where the statistics discipline is useful.

We deal with problems where we have to make decisions over time as new information is arriving. Most of our problems involve complex vectors of decisions (managing fleets of vehicles, allocating research dollars to projects, planning energy investments or the use of current energy resources) in the presence of different sources of uncertainty. We begin with a desire to understand how to control these systems, and then use this information to develop robust designs.