This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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635
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Type:
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Contributed
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Date/Time:
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Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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Abstract - #306605 |
Title:
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Robust Estimation of Mixtures of Heavy-Tailed Distributions
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Author(s):
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A. M. Santos*+ and Karen Kafadar
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Companies:
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IBM/University of Colorado Denver and Indiana University
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Address:
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11774 Melody Dr, Northglenn, CO, 80234,
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Keywords:
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robustness ;
finite mixtures ;
EM algorithm ;
biweight ;
h-distribution
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Abstract:
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We examine the robustness and efficiency of a variant of the EM algorithm for estimating parameters of mixtures of heavy-tailed distributions. Specifically, we use the biweight in an EM-like algorithm to estimate location, scale, and proportion of each component of a mixture of h-distributions. This family of long-tailed distributions, indexed by a tail-length parameter (h=0 corresponds to the Gaussian), provides a measure by which robustness and efficiency relative to the Gaussian can be assessed. We compare our results to those using the conventional EM-algorithm (in the context of a model that assumes Gaussian distributions, producing maximum likelihood estimates) and with Scott's L2E (2001) approach. Finally, we describe the application that motivated this research and our plans for future work.
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