Graphic modelling is a form of multivariate analysis that uses graphs to represent models.
These graphs display the structure of dependencies, both associational and causal …

As a powerful tool for analyzing full conditional (in-) dependencies between random
variables, graphical models have become increasingly popular to infer genetic networks …

This paper proposes a procedure for analysis of multidimensionality in graphical loglinear
Rasch models. The procedure combines exploratory techniques based on analysis of local …

Assume that cause-effect relationships between variables can be described as a directed
acyclic graph and the corresponding linear structural equation model. We consider the …

We specify some conditions for the identification of a multi‐factor model with correlated
residuals, uncorrelated factors and zero restrictions in the factor loadings. These conditions …

We study parameter identifiability of directed Gaussian graphical models with one latent
variable. In the scenario we consider, the latent variable is a confounder that forms a source …

This article shows how interfactor correlation is affected by error correlations. Theoretical
and practical justifications for error correlations are given, and a new equivalence class of …

Conditions are presented for different types of identifiability of discrete variable models
generated over an undirected graph in which one node represents a binary hidden variable …

We study criteria for identifiability of path analysis models with one hidden variable. We first
derive sufficient criteria for identification of models in which marginalisation is carried out …

A necessary and sufficient condition for the identification of a single-factor model with
correlated residuals is derived by studying the zero elements of their concentration matrix. In …