"Everything should be made as simple as possible, but not simpler." - Albert Einstein

Seminar Announcement

Elements of Statistical Learning for Complex Data Objects

Haonan Wang, Ph.D, Department of Statistics, Colorado State University, Fort Collins.

Monday, October 20, 2008

4:00 p.m. 223 Weber

ABSTRACT

Object oriented data, such as tree-structured data, random graphs,
manifold data and curve data, are frequently collected in many
scientific studies. Traditional statistical models for multivariate
data are built under Euclidean space setting. However, the elements of
object oriented data analysis reside in non-Euclidean spaces such as
Lie groups, or more complex spaces such as spaces of tree-structured
data. For example, two blood vessel systems differ in terms of
topological structures and geometric properties, i.e., overall length,
number of branches and branching orientation. A mathematical framework
for statistical analysis of object oriented data, including measures
of centrality, variability and a notion of curves, has been carefully
developed. The methodology is illustrated through applications to the
analysis of vectorcardiography data and brain blood vessel data.