Snedecor Lecture
Statistical
Laboratory
Iowa State University
DATE AND TIME:
Monday, March 20, 2006, 4:10 p.m.
PLACE: 319
Snedecor
SPEAKER: Jianqing Fan, Department of
Operation Research and Financial Engineering, Princeton University, Princeton,
NJ
TITLE: Statistical Challenges with High Dimensionality in
Feature Selection
ABSTRACT
Technological innovations have revolutionized
the process of scientific research and knowledge discovery. The
availability of massive data and challenges from frontiers of research and
development have reshaped statistical thinking, data analysis and theoretical
studies. The challenges of high-dimensionality arise in diverse fields of
sciences and the humanities, ranging from computational biology and health
studies to financial engineering and risk management. In all of these
fields, variable selection and feature extraction are crucial for knowledge
discovery. We first give a comprehensive overview of statistical challenges with
high dimensionality in these diverse disciplines. We then approach the
problem of variable selection and feature extraction using a unified framework:
penalized likelihood methods. Issues relevant to the choice of penalty
functions are addressed. We demonstrate that for a host of statistical
problems, as long as the dimensionality is not excessively large, we can
estimate the model parameters as well as if the best model is known in
advance. The persistence property in risk minimization is also
addressed. The applicability of such a theory and method to diverse
statistical problems is demonstrated. Other related problems with
high-dimensionality are also discussed.
COFFEE: 3:45
p.m., 104 Snedecor Hall
Seminar schedules and abstracts are
available via WWW:
http://www.stat.iastate.edu/
Jeanette La Grange
Department of Statistics
102
Snedecor
Iowa State University
Ames, IA 50010-1210
515 294-3440
(office)
515 294-4040 (fax)
http://www.stat.iastate.edu/directory/staff/jeanette.html