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 },
}

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