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Activity Number:
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119
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 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 - #307346 |
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Title:
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Nonparametric Mixture Regression
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Author(s):
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Alex Rojas*+ and Christopher Genovese and Larry Wasserman
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Companies:
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Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University
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Address:
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5000 Forbes Ave., Pittsburgh, PA, 15213,
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Keywords:
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conditional density estimation ; local likelihood ; mixture models ; EM algorithm
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Abstract:
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Conditional density estimation is a techniques that allow for a better understanding of the relationship between a response variable and a set of covariates in comparison with usual regression methods. Therefore, this technique is of great importance in many scientific fields where knowledge about conditional means, obtained by regression methods, is not enough to draw valuable conclusions of the problem at hand. In this paper we present a conditional density estimator based on finite mixture models and local likelihood estimation, which has the advantage of being easily interpretable.
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