Course Title:  Functional Data Analysis

Spring Semester 2002


Taught By:  J. S. Marron


Description:  Functional Data Analysis is an emerging 
subfield of statistics.  A way to understand the field is 
to think about the "atom" of a statistical analysis.  In a 
first course in statistics, atoms are numbers, and one 
analyzes the structure of populations of numbers.  In 
multivariate analysis, atoms are vectors.  In Functional 
Data Analysis, the atoms are more complex objects, e.g. 
curves (this case is closely related to "longitudinal data 
analysis"), images, and even shapes of objects in 3d.  
Understanding and using a set of tools for analyzing such 
data is the focus of the course.  Methods discussed will 
include related ideas from the old field of "pattern 
recognition" and the rather new area called "machine 
learning".  Driving examples come from medical imaging 
(where there is interest in a population analysis of 
various body parts), and from genetic micro-array analysis.  



Text Book:
required: none
recommended:  Ramsay, J. O. and Silverman, B. W. (1997) 
Functional Data Analysis, Springer.


Prerequisites:  One year of probability and statistics, at
the undergraduate level.