High-dimensional generation of Bernoulli random vectors

Reza Modarres

Statistics & Probability Letters, 2011, vol. 81, issue 8, 1136-1142

Abstract: The objective of this paper is to explore different modeling strategies to generate high-dimensional Bernoulli vectors. We 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. Each method has a domain of effectiveness. The latent model offers unpatterned correlation structures while the conditional mean and the mixture model provide computational feasibility for high-dimensional generation of MB vectors.

Keywords: Binary; Mixture; Latent; Dependence; Network (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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