@ARTICLE {ShipleyDouma2020,
AUTHOR = {Shipley, B. and Douma, J.C.},
JOURNAL = {Ecology},
TITLE = {Generalized AIC and chi-squared statistics for path models consistent with directed acyclic graphs},
YEAR = {2020},
NOTE = {cited By 0},
NUMBER = {3},
VOLUME = {101},
ABSTRACT = {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},
AFFILIATION = {Département de biologie, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; Centre for Crop Systems Analysis, Wageningen University, Droevendaalsesteeg 1, Wageningen, 6708 PB, Netherlands},
ART_NUMBER = {e02960},
AUTHOR_KEYWORDS = {Akaike Information Criterion; d-separation; directed acyclic graph; maximum likelihood; model selection; path analysis; piecewise SEM},
DOCUMENT_TYPE = {Article},
DOI = {10.1002/ecy.2960},
SOURCE = {Scopus},
URL = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081139803&doi=10.1002%2fecy.2960&partnerID=40&md5=bcb7a7d8607cfbf8d5798a8ee1cc3496},
}