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Abstract Details
Activity Number:
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642
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #304771 |
Title:
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Large-Sample Convergence of the Nonparametric Density Mixture Estimators
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Author(s):
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Michael Levine*+ and David Hunter
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Companies:
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Purdue University and Penn State University
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Address:
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Dept.of Statistics, HAAS Bldg, West Lafayette, IN, 47907-2066, United States
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Keywords:
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nonparametric ;
mixture ;
conditional independence ;
Em-type algorithm
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Abstract:
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Estimation of the nonparametric multivariate density mixtures is a relatively new area of research in mathematical statistics. Commonly, conditional independence for coordinates of the random vectors is assumed; such an assumption can be thought of as an extension of the independence assumption for longitudinal data where it is routinely assumed to be conditional upon the subject. A number of algorithms that can be used to estimate both weights and functional components in such a mixture has been recently suggested. While the empirical performance of these algorithms seems to be fairly impressive, their large sample properties have been almost completely unknown until now. In this presentation, we will present some of our recent results concerning asymptotic large-sample behavior for one such algorithm. The algorithm in question is an EM-type algorithm that maximizes the explicit objective function. This last fact turns out to be decisive in establishing the asymptotic properties of our algorithm.
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