This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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497
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #306367 |
Title:
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High-Dimensional Nonparametric Bayes Modeling via Nonlinear Latent Factor Models
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Author(s):
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David Dunson*+
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Companies:
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Duke University
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Address:
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219A Old Chemistry Building, Durham, NC, ,
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Keywords:
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Gaussian process ;
factor analysis ;
nonparametric Bayes ;
dimensionality reduction
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Abstract:
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It has become routine to encounter massive dimensional data in a broad variety of application areas. One useful tool for dimensionality reduction and modeling is sparse Bayesian latent factor analysis. This talk focuses on developing general frameworks for making such approaches more flexible, and hence facilitate a greater degree of dimensionality reduction as well as nonparametric density estimation and regression in massive dimensions. A class of infinite latent factor models is proposed, with the higher indexed factors having decreasing impact on the response, while allowing nonlinearities. Theoretical properties are considered and the methods are applied to simulated data and applications in genomics and machine learning.
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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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