People

Fan 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. Hisprimary 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.

In each of the areas mentioned above, he devotes most of his efforts to the search for intuitively appealing, model-free, robust nonparametric approaches and illustrates the approaches by real data and simulated examples. Modern statistical principles and modeling inevitably involve intensive computation, which is a part of the methodological research development. He is also very interested in developing foundational statistical theory and in providing fundamental insights to sophisticated statistical models. These include sampling theory, statisical learning theory, minimax theory, efficient semi-parametric modeling and nonlinear function estimation.