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Abstract Details
Activity Number:
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622
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #301868 |
Title:
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Studying Conditional Relationships via Partial Rank Correlation
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Author(s):
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Calandra R. Tate Moore*+
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Companies:
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The City University of New York
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Address:
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College of Staten Island, Staten Island, NY, 10314,
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Keywords:
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Cross-classified Data ;
Simulation ;
Grouping Effects
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Abstract:
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Suppose that X and Y are random variables whose relationship is of interest but may be quite different within different levels of a discrete group-labeling variable Z. How can the true degree of relationship between X and Y conditionally given Z be summarized nonparametrically ?
To answer this question, this paper presents and compares two new definitions for partial rank correlations: (1) S1, a rank correlation computed from X and Y after linearly correcting them by their group means within levels of Z, and (2) S2, a weighted linear combination of within Z-group rank correlations.
We investigate the comparative asymptotic performance of statistics S1 and S2, and apply them in detecting within group relationships in data obtained from a machine-translation experiment. The large-sample power of each statistic to detect real within group relationships is determined and illustrated by simulation for a broad range of within group dependence models. Further modifications of these statistics to take account of group-Z scale-factors are considered for various potential applications.
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