Abstract Details
Activity Number:
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610
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2014 : 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 #313817
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Title:
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A New Semiparametric Approach to Finite Mixture of Regressions Using Penalized Regression via Fusion
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Author(s):
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Erin Austin*+ and Wei Pan and Xiaotong Shen
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Companies:
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University of Minnesota and University of Minnesota and University of Minnesota
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Keywords:
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FMR ;
Group LASSO ;
Group TLP ;
Semiparametric
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Abstract:
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For some modeling problems a population may be better assessed as an aggregate of unknown subpopulations, each with a distinct relationship between a response and associated variables. The finite mixture of regressions (FMR) model, where an outcome is derived from one of a finite number of linear regression models, is a natural tool in this setting. In this article we first propose a novel penalized regression approach, then we demonstrate how it can, in some types of problems, better identify subpopulations and their corresponding models than a semiparametric FMR method. Our new method fits models for each person via grouping pursuit, utilizing a new group truncated L1-penalty (gTLP) that shrinks differences between estimated parameter vectors. The methodology causes the individuals' regression coefficients to cluster into a few common models, in turn revealing previously unknown subpopulations. In fact, by varying the penalty strength, the new method can reveal a hierarchical structure among the subpopulations that can be useful in exploratory analysis. Real data analysis shows the performance of the method is promising.
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Authors who are presenting talks have a * after their name.
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