Statistics Workshop 2006
Statistical Discovery 2007

Day of Statistics 2008

 


2jqfan3at4princeton5edu
(remove numbers)
E-332 E-Quad
609-258-8442

Department of Oper Res and Fin. Eng
Princeton University
Princeton, NJ 08540

Statistics at Princeton
Statistics at ORFE
Statistics Lab

My Views

 

 

 

 

 

 

 

 

 

 

 

 

My pride:
Nikhadakiera: The Improvisers by M. S. Fan

                        (American Statistical Association, International Biometric Society
                        --- Eastern North American Region and Western North American Region
                        Institute of Mathematical Statistics and Statistical Society of Canada)
                  
Related news (EnglishChinese, Photo)

      Fellow, American Association for Advancement of Science.
      Fellow, Institute of Mathematical Statistics.
      Fellow, American Statistical Association.
      Elected Member, International Statistical Institute
      Hettleman Prize for Artistic and Scholarly Achievement, 1996, University of North Carolina.
      NSF Postdoctoral Fellowship, 1993-96.
      Evelyn Fix Memorial Medal, University of California at Berkeley, 1989.

    Professional Services

    Current Research Directions

    • Financial Econometrics and Risk Management
    • Computational Biology
    • High-dimensional Large-Scale modeling
    • Nonparametric and semiparametric modeling
    • Nonlinear time series
    • Analysis of longitudinal data
    • Model selections
    • Survival Analysis

    Research Interests

      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. 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.

      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.

      Recently, Fan is particularly interested in financial econometrics, risk management, computational biology, biostatistics, high-dimensional data-analytic modeling and inferences, nonlinear time series, analysis of longitudinal and functional data,  and other interdisciplinary collaborations.

    Selected Publications: Click Here

    Revised January 1, 2006

    Back to Faculty Directory

    To ORFE Home Page