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Activity Number:
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170
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #304968 |
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Title:
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Inference for Multivariate Normal Mixtures and Its Applications
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Author(s):
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Xianming Tan*+
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Companies:
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Penn State University
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Address:
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The Methodology Center, State College, PA, 16801,
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Keywords:
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Multivariate normal mixtures ; maximum penalized likelihood estimator ; strong consistency ; longitudinal data analysis
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Abstract:
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Multivariate normal mixtures provide a flexible model for high-dimensional data. They are widely used in statistical genetics, statistical finance, and other disciplines. In this talk, we recommend a penalized likelihood method for estimating the mixing distribution to avoid the unboundedness of ordinary likelihood function. We show that the maximum penalized likelihood estimator is strongly consistent when the number of components has a known upper bound. Extensive simulations are conducted to explore the effectiveness and the practical limitations of both the new method and the ratified maximum likelihood estimators. Guidelines are provided based on the simulation results. The potential applications of multivariate normal mixtures in longitudinal data analysis will also be discussed.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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