This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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122
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #307070 |
Title:
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Semiparametric Mixtures of Regressions
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Author(s):
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David Hunter*+
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Companies:
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Penn State
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Address:
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326 Thomas Building, University Park, PA, 16802, USA
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Keywords:
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Mixture models ;
Semi-parametric mixtures
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Abstract:
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Mixtures of regressions may be appropriate in cases where a response variable is thought to depend on one or more explanatory variables, but there exist different categories of explanatory-response pairs for which different regression relationships exist and we do not observe the category of each pair. Mixture-of-regressions models have appeared in the statistical literature, though not extensively and generally only in a fully parametric form. After a brief discussion of the parametric case (and its implementation in the mixtools package for R), we consider relaxing the parametric assumptions of a standard mixture of regressions in several directions: Allowing the error structure to be unspecified, allowing the regression curve to be unspecified, and allowing the mixing proportions to vary as a function of the predictors.
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Authors who are presenting talks have a * after their name.
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