Maximum likelihood fitting of acyclic directed mixed graphs to binary data
arXiv:1203.3479v125 citations
Originality Synthesis-oriented
AI Analysis
This work addresses a gap in statistical modeling for researchers dealing with latent variable structures in binary datasets, though it appears incremental as it extends existing methods to a specific data type.
The authors tackled the problem of fitting acyclic directed mixed graphs (ADMGs) to binary data, achieving the first method for maximum likelihood estimation in this context.
Acyclic directed mixed graphs, also known as semi-Markov models represent the conditional independence structure induced on an observed margin by a DAG model with latent variables. In this paper we present the first method for fitting these models to binary data using maximum likelihood estimation.