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Abstract Details
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Activity Number:
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635
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Type:
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Invited
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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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Biometrics Section
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| Abstract - #300240 |
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Title:
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Sliced Mean Variance Covariance Inverse Regression
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Author(s):
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Simon Sheather*+ and Charles Lindsey and Joseph McKean
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Companies:
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Texas A & M University and STATA Corporation and Western Michigan University
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Address:
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Department of Statistics, College Station, TX, 77843,
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Keywords:
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inverse regression ;
nonparametric statistics ;
dimension reduction ;
visualization
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Abstract:
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The sliced mean variance-covariance inverse regression (SMVCIR) algorithm takes grouped multivariate data as input and transforms it to a new co-ordinate system where the group mean, variance, and covariance differences are more apparent. A dimensionality test is developed for SMVCIR, telling us how many coordinates the new discrimination co-ordinate system needs. Simulations are shown to verify accuracy of the new test. In addition, a variable selection method is provided in order to assessthe importance of particular input variables. Examples are provided, contrasting SMVCIR with sliced average variance estimation (SAVE) and sliced inverse regression (SIR).
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