Statistics | Functional Data Analysis
S481 | 10231 | Karen Kafadar


This course will introduce students to methods for analyzing
functional data --- i.e., data that are entire curves, rather than
single observations or vectors of several measurements. Such data
objects involve numerous highly-related points per object, and the
methods for analyzing them make explicit use of data objects as
functions. In contrast to multivariate analysis (multiple values per
object that different degrees of associations) and longitudinal
analysis or "panel studies" (where measurements are repeated on an
individual at only a few time points), the methods here treat the
object as a function.  We discuss these methods which include
graphical displays, summaries, analogs of conventional analyses
(analysis of variance, principal components), with an emphasis on
applications.  The two required books will address both these goals
(methodology and applications).  The course is valuable for both data
researchers whose data are entire functions (gait, spectra, etc.) as
well as students interested in participating in a relatively new and
important area of statistical research.

Required textbooks:

James Ramsay and Bernard Silverman,
Functional Data Analysis (FDA), Springer.

James Ramsay and Bernard Silverman,
Applied Functional Data Analysis (AFDA), Springer.