Frequentist Model Averaging in Structural Equation Modelling
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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.
Keywordsmodel selection post-selection inference coverage probability local asymptotic goodness-of-fit
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.
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