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Statistics Colloquium
December 1 Friday, 11 a.m.
Alavi Commons Room, 6625 Everett Tower
Hyun Bin Kang
Ph.D. Candidate, Department of Statistics
The Pennsylvania State University
Abstract:
Many scientific areas are faced with the challenge of extracting
information from large, complex, and highly structured data sets.
A great deal of modern statistical work focuses on developing tools
for handling such data.
This paper presents a new subfield of functional data analysis,
FDA, which we call Manifold Data Analysis, or MDA.
MDA is concerned with the statistical analysis of samples
where one or more variables measured on each unit is a
manifold, thus resulting in as many manifolds as we have units.
We propose a framework that converts manifolds into
functional objects, an efficient 2-step functional
principal component method, and a manifold-on-scalar
regression model. This work is motivated by an
anthropological application involving 3D facial
imaging data, which will be discussed extensively.
The proposed framework is used to understand how
individual characteristics, such as age and genetic
ancestry, influence the shape of the human face.
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All statistics graduate students are expected to attend.