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Abstract Details
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Activity Number:
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173
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 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 - #303418 |
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Title:
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High-Dimensional Generation of Bernoulli Random Vectors
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Author(s):
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Reza Modarres*+
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Companies:
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The George Washington University
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Address:
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Department of Statistics, Washington DC, DC, 20052,
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Keywords:
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Multivariate ;
Bernoulli ;
Mixture Model ;
Latent Variable ;
Random Vectors
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Abstract:
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We explore different modeling strategies to generate high dimensional Bernoulli vectors, discuss the multivariate Bernoulli (MB) distribution, probe its properties and examine three models for generating random vectors. A latent multivariate normal model whose bivariate distributions are approximated with Plackett distributions with univariate normal distributions is presented. A conditional mean model is examined where the conditional probability of success depends on previous history of successes. A mixture of Beta distributions is also presented that expresses the probability of the MB vector as a product of correlated binary random variables.
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Authors who are presenting talks have a * after their name.
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