Abstract Details
Activity Number:
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180
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 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 - #308583 |
Title:
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ANOVA for Symbolic Data
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Author(s):
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Yi Chen*+ and Lynne Billard
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Companies:
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University of Georgia and University of Georgia
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Keywords:
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symbolic data ;
symbolic covariance method ;
linear regression ;
ANOVA
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Abstract:
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Data we have always analyzed are classical data. Observations of classical data on p random variables are points in p-dimensional space Rp. By contrast, symbolic data with p variables are p-dimensional hypercubes in Rp. List, interval and histogram data are common types of symbolic data. Classical data can be aggregated to symbolic data for the purpose of reducing computing time and obtaining more reasonable results. We mainly focus on interval-valued data, especially the regression method for interval-valued data. Symbolic Covariance Method (SCM) has been applied to interval-valued data with quantitative explanatory variable, but not to data with qualitative explanatory variable. In this paper, we find regression approaches to ANOVA for interval-valued data using SCM. Algorithms of these approaches are coded in R and applied to both simulated and practical data.
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