Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 562936
Title Generalized AIC and chi-squared statistics for path models consistent with directed acyclic graphs
Author(s) Shipley, Bill; Douma, Jacob C.
Source Ecology 101 (2020)3. - ISSN 0012-9658
DOI https://doi.org/10.1002/ecy.2960
Department(s) Crop and Weed Ecology
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2020
Keyword(s) Akaike Information Criterion - d-separation - directed acyclic graph - maximum likelihood - model selection - path analysis - piecewise SEM
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.

Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.