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The logistic conditionals binary family

Schäfer, Christian (2011), The logistic conditionals binary family. https://basepub.dauphine.fr/handle/123456789/7434

Type
Document de travail / Working paper
External document link
http://fr.arxiv.org/abs/1111.0576
Date
2011
Publisher
Université Paris-Dauphine
Published in
Paris
Pages
16
Metadata
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Author(s)
Schäfer, Christian
Abstract (EN)
We discuss a parametric family of binary distributions for modeling and generating multivariate binary data with strong dependencies in dimensions too large for exhaustive enumeration of the state space. The proposed parametric family is shown to encompass any feasible combination of mean vector and correlation matrix. The approach goes beyond the range of dependencies achievable with methods discussed heretofore in the literature which we systematically review in this paper. We can both sample from the parametric family and evaluate its mass function point-wise which allows for immediate use in the context of stochastic optimization, importance sampling or Markov chain algorithms.
Subjects / Keywords
Logistic conditionals; Correlated binary data; Multivariate binary data; Binary parametric families

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