PreprintCC BY 4.0arXiv
A binary factor model
Abstract
The orthogonal factor model has been a very useful tool in uncovering covariance structures in a set of variables through a smaller set of underlying factors. This old model is suitable for continuous variables with unbounded support, since the most common assumption for the observables and the factors is multivariate normality. In this work, we propose a factor model for binary data. Factors are negative dependent, so they avoid each other. We study the theoretical properties of the model and carry out a full Bayesian inference. We illustrate the performance of our proposal with simulated and real data sets and compare with the traditional benchmark.
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BibTeX
@misc{nietobarajas2026binary,
title = {{A binary factor model}},
author = {Luis E. Nieto-Barajas},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.09587},
eprint = {2609.09587},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.09587},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A binary factor model
AU - Luis E. Nieto-Barajas
PY - 2026
DA - 2026/09/09
PB - arXiv
DO - 10.48550/arxiv.2609.09587
UR - https://arxiv.org/abs/2609.09587
AB - The orthogonal factor model has been a very useful tool in uncovering covariance structures in a set of variables through a smaller set of underlying factors. This old model is suitable for continuous variables with unbounded support, since the most common assumption for the observables and the factors is multivariate normality. In this work, we propose a factor model for binary data. Factors are negative dependent, so they avoid each other. We study the theoretical properties of the model and carry out a full Bayesian inference. We illustrate the performance of our proposal with simulated and real data sets and compare with the traditional benchmark.
ER -