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Frequentist Model Averaging in Structural Equation Modelling

Shaobo Jin () and Sebastian Ankargren ()
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Shaobo Jin: Uppsala University

Psychometrika, 2019, vol. 84, issue 1, No 5, 84-104

Abstract: 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 $$\chi ^2$$ χ 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 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11336-018-9624-y

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