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Activity Number:
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361
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #319729
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View Presentation
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Title:
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An Approach to the Multivariate Two-Sample Problem Using Classification and Regression Trees and Minimum-Weight Spanning Subgraphs
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Author(s):
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David Ruth* and Samuel Buttrey and Lyn Whitaker
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Companies:
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and Naval Postgraduate School and Naval Postgraduate School
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Keywords:
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Multivariate statistics ;
Multivariate two-sample problem ;
Graph-based test ;
Classification and regression trees ;
Nonparametric statistics
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Abstract:
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The multivariate two-sample problem is one of continued interest in statistics. Approaches to this problem normally require a dissimilarity measure on the observation sample space; such measures are typically restricted to numeric variables. In order to accommodate both categorical and numeric variables, we use a new dissimilarity measure based on a set of classification and regression trees. We briefly discuss this new measure and then incorporate it into in a recently developed graph-based multivariate test. The test statistic counts the number of intergroup edges in a minimum-weight regular spanning subgraph; unequal distributions will tend to result in fewer edges in this count. Test performance is examined via simulation study, and test efficacy investigated using real-world data.
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Authors who are presenting talks have a * after their name.
