Problem
Research questions and friction points this paper is trying to address.
binary data
factor model
orthogonal factor model
A binary factor model
🤖 AI Summary
本文提出了一种适用于二元数据的因子模型，通过负相关因子避免重叠，并采用贝叶斯推断方法进行研究。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
binary data
negative dependent factors
Bayesian inference
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