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- Title
Generalized AIC and chi‐squared statistics for path models consistent with directed acyclic graphs.
- Authors
Shipley, Bill; Douma, Jacob C.
- Abstract
We explain how to obtain a generalized maximum‐likelihood chi‐square statistic,XML2, and a full‐model Akaike Information Criterion (AIC) statistic for piecewise structural equation modeling (SEM); that is, structural equations without latent variables whose causal topology can be represented as a directed acyclic graph (DAG). The full piecewise SEM is decomposed into submodels as a Markov network, each of which can have different distributional assumptions or functional links and that can be modeled by any method that produces maximum‐likelihood parameter estimates. The generalized XML2 is a function of the difference in the maximum likelihoods of the model and its saturated equivalent and the full‐model AIC is calculated by summing the AIC statistics of each of the submodels.
- Subjects
DIRECTED acyclic graphs; LATENT variables; AKAIKE information criterion; STRUCTURAL equation modeling; STATISTICS; PATH analysis (Statistics)
- Publication
Ecology, 2020, Vol 101, Issue 3, p1
- ISSN
0012-9658
- Publication type
Article
- DOI
10.1002/ecy.2960