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Abstract Details
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Activity Number:
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574
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #301145 |
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Title:
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Robust Estimation of Censored Mixture Models
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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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SimpleGeo/University of Colorado at Denver and Indiana University
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Address:
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, , ,
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Keywords:
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robustness ;
finite mixtures ;
EM algorithm ;
biweight
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Abstract:
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This research is motivated by the common problems of censoring and mixtures in real-world data. Measurement limitations cause many datasets in healthcare, economics, business and other research areas to have inexact results at the upper and lower bounds of the data. Additionally, many datasets contain multiple populations, resulting in mixture distributions. We demonstrate both a MLE method for fitting this type of data and a robust extension with comparable estimation properties and improved computational time. We evaluate both methods on multiple scenarios, including the possibility that the underlying distributions are not Gaussian, but have heavier tails.
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