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Activity Number: 66 - Higlights from the Journal Stat
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: International Statistical Institute
Abstract #329225
Title: Linear Structural Equation Models with Non-Gaussian Errors
Author(s): Y. Samuel Wang*
Companies: University of Washington
Keywords: structural equation models; empirical likelihood
Abstract:

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 (EL) 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 EL and the proposed modifications may increase statistical efficiency and improve assessment of significance. We will also briefly discuss extensions of this work involving causal discovery.


Authors who are presenting talks have a * after their name.

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