ShipleyDouma2020
Référence
Shipley, B., Douma, J.C. (2020) Generalized AIC and chi-squared statistics for path models consistent with directed acyclic graphs. Ecology, 101(3). (Scopus 
)
Résumé
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
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@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 },
}
