Abstract Details
Activity Number:
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373
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Type:
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Contributed
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Date/Time:
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Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #316854
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Title:
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Dependence Modeling via Voronoi-Based Cluster Analysis
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Author(s):
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Ricardo Couto* and Luiz Duczmal and Denise Burgarelli and Felipe Álvares da Silva
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Companies:
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IBMEC and Universidade Federal de Minas Gerais and UFMG and UFMG
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Keywords:
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Non-parametric algorithm ;
Scan statistics ;
Tesselation ;
Crisis
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Abstract:
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The linear correlation coefficient and more recently copulas have been used to model the dependence structure in bivariate case. Although copulas have overcome many drawbacks of the linear correlation coefficient, copula selection is still an open discussion as measures to compare different copulas functions are not unanimous. This work proposes a new non-parametric approach to model dependence structure using the weighted Voronoi distance as metric. The spatial tessellation of the plane uses the principles of the Prospective Space-Time Scan combined with a weighting procedure based on Voronoi cells densities. The advantages are two-fold. First, the time series structure is respected and no independence is presumed within data. Second, any distribution of the data and any dependence function is allowed. An inference procedure is presented and applied to verify the method using real data for the 2007-8 financial crisis in the US market and compared with traditional linear coefficient and copula families. The proposed methodology showed signals of crisis earlier than other methods.
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Authors who are presenting talks have a * after their name.
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