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Activity Number:
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304
- Statistical Learning: Dimension Reduction
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #324699
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View Presentation
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Title:
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Information Tests on Statistical Submanifolds
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Author(s):
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Michael Trosset* and Carey E Priebe
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Companies:
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Indiana University and Johns Hopkins University
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Keywords:
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restricted inference ;
dimension reduction ;
information geometry ;
minimum distance test
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Abstract:
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It is well-known that Fisher information induces a Riemannian structure on a statistical manifold. We use this fact to derive information tests that exploit the Riemannian geometry of dimension-restricted statistical submanifolds. Information tests are locally asymptotically equivalent to various classical tests; however, despite their conceptual appeal, they are of limited practical value. We propose discrete approximations of information tests and demonstrate their effectiveness on several examples.
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Authors who are presenting talks have a * after their name.
