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Psychometrika

, Volume 84, Issue 1, pp 84–104 | Cite as

Frequentist Model Averaging in Structural Equation Modelling

  • Shaobo JinEmail author
  • Sebastian Ankargren
Article

Abstract

Model selection from a set of candidate models plays an important role in many structural equation modelling applications. However, traditional model selection methods introduce extra randomness that is not accounted for by post-model selection inference. In the current study, we propose a model averaging technique within the frequentist statistical framework. Instead of selecting an optimal model, the contributions of all candidate models are acknowledged. Valid confidence intervals and a \(\chi ^2\) test statistic are proposed. A simulation study shows that the proposed method is able to produce a robust mean-squared error, a better coverage probability, and a better goodness-of-fit test compared to model selection. It is an interesting compromise between model selection and the full model.

Keywords

model selection post-selection inference coverage probability local asymptotic goodness-of-fit 

Notes

Acknowledgements

We would like to thank the reviewers for providing valuable comments. Shaobo Jin was partly supported by Vetenskapsrådet (Swedish Research Council) under the contract 2017-01175.

Supplementary material

11336_2018_9624_MOESM1_ESM.r (38 kb)
Supplementary material 1 (R 37 KB)

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Copyright information

© The Psychometric Society 2018

Authors and Affiliations

  1. 1.Department of StatisticsUppsala UniversityUppsalaSweden

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