%0 Journal Article
%A Shipley, B.
%A Douma, J.C.
%B Ecology
%T Generalized AIC and chi-squared statistics for path models consistent with directed acyclic graphs
%D 2020
%Z cited By 0; doi=(10.1002/ecy.2960)
%N 3
%V 101
%X We explain how to obtain a generalized maximum-likelihood chi-square statistic, X2 ML, 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 X2 ML 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. © 2019 by the Ecological Society of America
%I Département de biologie, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; Centre for Crop Systems Analysis, Wageningen University, Droevendaalsesteeg 1, Wageningen, 6708 PB, Netherlands
Scopus
%U https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081139803&doi=10.1002%2fecy.2960&partnerID=40&md5=bcb7a7d8607cfbf8d5798a8ee1cc3496
%F ShipleyDouma2020
%3 BibTeX type = ARTICLE