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Abstract Details
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Activity Number:
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673
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #302424 |
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Title:
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Dimension Reduction of Multivariate Autocorrelated Processes
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Author(s):
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Xuan Huang*+
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Companies:
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University of Alabama at Birmingham
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Address:
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1150 10th Avenue South, BEC 216, Birmingham, AL, 35294-4460,
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Keywords:
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Dimension Reduction ;
Principal Components Analysis ;
Time series ;
Autocorrelation
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Abstract:
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In traditional multivariate literature, Principal Components Analysis (PCA) is the standard tool for dimension reduction. For autocorrelated processes, however, PCA fails to take into account the time structure information. It is arguable that PCA is still the best choice. In this presentation I propose an enhanced dimension reduction method which by design takes into account both cross-correlation and autocorrelation information. I demonstrate it through case studies and simulations.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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