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Activity Number: 175 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323229
Title: Model-Based Clustering with Application of Copula for Symbolic Data
Author(s): Wenhao Pan* and lynne Billard
Companies: University of Georgia and University of Georgia
Keywords: Symbolic Data ; Model-based Clustering ; Copulas
Abstract:

Contemporary data sets can be too large or complex for traditional statistical methods to handle. One approach is to use symbolic data first introduced by Diday (1987). Our interest is the study of model-based clustering for symbolic data, especially for distributions (i.e., observations are not the single numerical point values). We will describe symbolic data and consider differences between symbolic data and classical data. When we only have the marginal distributions, we do not know the dependence relationship between random variables. One approach to measure these dependences is that of Vrac et al. (2012) in which a copula function is used to describe the cumulative joint distribution function of random variables in a mixture model. We will review these ideas.


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

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