On parametric families for sampling binary data with specified mean and correlation
Methodology
2012-04-09 v4 Computation
Abstract
We discuss a class of binary parametric families with conditional probabilities taking the form of generalized linear models and show that this approach allows to model high-dimensional random binary vectors with arbitrary mean and correlation. We derive the special case of logistic conditionals as an approximation to the Ising-type exponential distribution and provide empirical evidence that this parametric family indeed outperforms competing approaches in terms of feasible correlations.
Cite
@article{arxiv.1111.0576,
title = {On parametric families for sampling binary data with specified mean and correlation},
author = {Christian Schäfer},
journal= {arXiv preprint arXiv:1111.0576},
year = {2012}
}