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Abstract Details
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Activity Number:
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129
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #305307 |
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Title:
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Inference on Random Graphs with Classified Edge Attributes
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Author(s):
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Michael Trosset*+ and William David Brinda and Shantanu Jain
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Companies:
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Indiana University and Yale University and Indiana University
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Address:
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Statistics House, Bloomington, IN, 47408, United States
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Keywords:
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comparison of experiments ;
confusion matrices ;
stochastic matrices ;
partial ordering
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Abstract:
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We test simple hypotheses about a random graph with a fixed set of vertices and random edges, each of which possesses one of K mutually exclusive attributes. The edge attributes are inferred by means of a fallible classifier. Suppose that E and F are the confusion matrices of two such classifiers. Using results from statistical decision theory, we demonstrate that, if there exists a K x K stochastic matrix R such that ER = F, then most powerful (MP) tests based on E are necessarily more powerful than MP tests based on F. By means of an example, we also demonstrate that entry-wise superiority of E to F does not guarantee that an MP test based on E is more powerful than an MP test based on F.
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