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Activity Number:
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30
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #304246 |
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Title:
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A Multivariate Likelihood-Tuned Density Estimator
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Author(s):
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Yeojin Chung*+ and Bruce G. Lindsay
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Companies:
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Penn State University and Penn State University
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Address:
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422 Thomas Bldg, University Park, PA, 16802,
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Keywords:
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density estimation ; nonparametric ; multivariate density estimation
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Abstract:
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We consider an improved multivariate nonparametric density estimator which arises from treating the kernel density estimator as an element of the model that consists of all mixtures of the kernel, continuous or discrete. One can obtain the kernel density estimator with "likelihood-tuning" by using the uniform density as the starting value in an EM algorithm. The second tuning leads to a fitted density with higher likelihood than the kernel density estimator. In the univariate case, the two-step likelihood-tuned density estimator reduces asymptotic bias and performs robustly against a type of the true density. We compare the performance of the new density estimator with other modified density estimators in higher dimensions.
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