bibtype;bibtexkey;abstract;address;annote;assignee;author;booktitle;chapter;crossref;comments;day;dayfiled;doi;edition;editor;eid;file;howpublished;institution;journal;key;keywords;language;lastchecked;month;note;number;organization;owner;pages;part;publisher;review;revision;school;series;timestamp;title;type;url;volume;year;yearfiled
"ARTICLE";"ShipleyDouma2020";"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";;;;"Shipley, B. and Douma, J.C.";;;;;;;"10.1002/ecy.2960";;;;;;;"Ecology";;;;;;"cited By 0";"3";;;;;;;;;;;"Generalized AIC and chi-squared statistics for path models consistent with directed acyclic graphs";;"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081139803&doi=10.1002%2fecy.2960&partnerID=40&md5=bcb7a7d8607cfbf8d5798a8ee1cc3496";"101";"2020";;