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Abstract Details
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Activity Number:
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249
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #301602 |
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Title:
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Principal Component Analysis for Interval Data - Perspective
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Author(s):
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Lynne Billard*+ and Jennifer Le-Rademacher
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Companies:
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University of Georgia and Medical College of Wisconsin
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Address:
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Department of Statistics, Athens , GA, 30602, USA
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Keywords:
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vertices method ;
symbolic method ;
vertex contribution
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Abstract:
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Although interval data occur naturally in their own right (such as species data), they will become more and more ubiquitous as contemporary computer capabilities generate massively large data sets necessitating aggregation in some way. We look at this phenomena first. Then we provide a perspective on available methods for principal component analyses (PCA) of interval data, and how results differ from and expand upon those for traditional PCA on classical point observations.
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