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Activity Number: 361
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319729 View Presentation
Title: An Approach to the Multivariate Two-Sample Problem Using Classification and Regression Trees and Minimum-Weight Spanning Subgraphs
Author(s): David Ruth* and Samuel Buttrey and Lyn Whitaker
Companies: and Naval Postgraduate School and Naval Postgraduate School
Keywords: Multivariate statistics ; Multivariate two-sample problem ; Graph-based test ; Classification and regression trees ; Nonparametric statistics

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.

Authors who are presenting talks have a * after their name.

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