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Abstract Details
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Activity Number:
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174
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #304521 |
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Title:
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A Simple Distribution-Free Algorithm for Generating Simulated High-Dimensional Correlated Data with an Autoregressive Structure
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Author(s):
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Andres Azuero*+ and Hemant K Tiwari and David T Redden
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Companies:
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The University of Alabama at Birmingham and The University of Alabama at Birmingham and The University of Alabama at Birmingham
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Address:
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1500 33rd St S, Birmingham, AL, 35205-2574, United States
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Keywords:
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simulation ;
high-dimensional data ;
fractile correlations ;
distribution-free methods
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Abstract:
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A distribution-free method to generate high-dimensional sequences of dependent variables with an autoregressive structure is presented. The quantile or fractile correlation (i.e. the moment correlation of the quantiles) is used as measure of dependence among a set of contiguous variables. The proposed algorithm breaks the sequence in small parts and avoids having to define one large correlation matrix for the entire high-dimensional sequence of variables. Simulations based on proteomics data are presented. Results suggest that negligible or no loss of fractile correlation occurs by splitting the generation of a sequence into small parts.
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