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Empirical likelihood for linear structural equation models with dependent errors
Author(s) -
Wang Y. Samuel,
Drton Mathias
Publication year - 2017
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.169
Subject(s) - inference , empirical likelihood , gaussian , structural equation modeling , maximum likelihood , statistical inference , mathematics , computer science , econometrics , empirical research , statistics , artificial intelligence , physics , quantum mechanics
We consider linear structural equation models that are associated with mixed graphs. The structural equations in these models only involve observed variables, but their idiosyncratic error terms are allowed to be correlated and non‐Gaussian. We propose empirical likelihood procedures for inference and suggest several modifications, including a profile likelihood, in order to improve tractability and performance of the resulting methods. Through simulations, we show that when the error distributions are non‐Gaussian, the use of empirical likelihood and the proposed modifications may increase statistical efficiency and improve assessment of significance. Copyright © 2017 John Wiley & Sons, Ltd.