Forskning
Udskriv Udskriv
Switch language
Rigshospitalet - en del af Københavns Universitetshospital
Udgivet

A two-stage estimation procedure for non-linear structural equation models

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. Men with high serotonin 1B receptor binding respond to provocations with heightened amygdala reactivity

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Genetic influence on the associations between IGF-I and glucose metabolism in a cohort of elderly twins

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Testosterone levels in healthy men correlate negatively with serotonin 4 receptor binding

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Serotonergic neurotransmission in emotional processing: New evidence from long-term recreational poly-drug ecstasy use

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Vis graf over relationer

Applications of structural equation models (SEMs) are often restricted to linear associations between variables. Maximum likelihood (ML) estimation in non-linear models may be complex and require numerical integration. Furthermore, ML inference is sensitive to distributional assumptions. In this article, we introduce a simple two-stage estimation technique for estimation of non-linear associations between latent variables. Here both steps are based on fitting linear SEMs: first a linear model is fitted to data on the latent predictor and terms describing the non-linear effect are predicted by their conditional means. In the second step, the predictions are included in a linear model for the latent outcome variable. We show that this procedure is consistent and identifies its asymptotic distribution. We also illustrate how this framework easily allows the association between latent variables to be modeled using restricted cubic splines, and we develop a modified estimator which is robust to non-normality of the latent predictor. In a simulation study, we compare the proposed method to MLE and alternative two-stage estimation techniques.

OriginalsprogEngelsk
TidsskriftBiostatistics
Vol/bind21
Udgave nummer4
Sider (fra-til)676-691
Antal sider16
ISSN1465-4644
DOI
StatusUdgivet - 1 okt. 2020

Bibliografisk note

© The Author 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

ID: 61117468