9 citations · 14 across the 12 of their papers we have counts for
6 papers · 1 filter
Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variables
Nils Sturma, Mathias Drton
We develop a criterion to certify whether causal effects are identifiable in linear structural equation models with latent variables. Linear structural equation models correspond t…
Matching Criterion for Identifiability in Sparse Factor Analysis
Nils Sturma, Miriam Kranzlmueller, Irem Portakal +1
Factor analysis models explain dependence among observed variables by a smaller number of unobserved factors. A main challenge in confirmatory factor analysis is determining whethe…
Singularity-agnostic incomplete U-statistics for testing polynomial constraints in Gaussian covariance matrices
Dennis Leung, Nils Sturma
Testing the goodness-of-fit of a model with its defining functional constraints in the parameters could date back to Spearman (1927), who analyzed the famous "tetrad" polynomial in…
Mixtures of Discrete Decomposable Graphical Models
Yulia Alexandr, Jane Ivy Coons, Nils Sturma
We study mixtures of decomposable graphical models, focusing on their ideals and dimensions. For mixtures of clique stars, we characterize the ideals in terms of ideals of mixtures…
Algebraic Sparse Factor Analysis
Mathias Drton, Alexandros Grosdos, Irem Portakal +1
Factor analysis is a statistical technique that explains correlations among observed random variables with the help of a smaller number of unobserved factors. In traditional full f…
Half-Trek Criterion for Identifiability of Latent Variable Models
Rina Foygel Barber, Mathias Drton, Nils Sturma +1
We consider linear structural equation models with latent variables and develop a criterion to certify whether the direct causal effects between the observable variables are identi…