Abstract Details
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Activity Number:
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366
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #309696 |
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Title:
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Two-Sample Tests for High-Dimensional Binary Data
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Author(s):
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Amanda Peterson*+ and Junyong Park
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Companies:
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UMBC and UMBC
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Keywords:
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high dimensional data
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Abstract:
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In this talk, we will discuss methods for performing a two-sample test on high-dimensional multivariate binary data. As it is well known, the curse of dimensionality makes these types of problems challenging. We applied a random projection method to the multivariate binary data using the method in conjunction with the classic Hotelling's T^2 statistic and also an Edgeworth expansion. Additionally, we will discuss alternative testing procedures that we considered for the case of sparse data and also popular high-dimensional testing methods proposed by others. We will show a comparison of their outcomes in different scenarios via simulation experiments.
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Authors who are presenting talks have a * after their name.
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