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Activity Number:
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425
- SPEED: Reliable Statistical Learning and Data Science
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 3:05 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #325178
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Title:
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Expected Conditional HSIC for Testing Independence
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Author(s):
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Chenlu Ke* and Xiangrong Yin
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Companies:
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and University of Kentucky
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Keywords:
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Independence testing ;
Reproducing kernel Hilbert space ;
kernel method
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Abstract:
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We propose a novel conditional Hilbert-Schmidt Independence Criterion for testing independence between two random vectors. The relation between our index and a conditional version of distance covariance measure is studied. Two empirical estimates and corresponding independence tests are developed under different scenarios. Finally, we illustrate the advantages of our method by simulations across a variety of settings and real data applications.
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Authors who are presenting talks have a * after their name.
