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 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)
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