psychometrika https://doi.org/10.1007/s11336-018-9624-y Shaobo Jin and Sebastian Ankargren UPPSALA UNIVERSITY 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 contri- butions of all candidate models are acknowledged. Valid conﬁdence intervals and a χ 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. Key words: model selection, post-selection inference, coverage probability, local asymptotic, goodness- of-ﬁt. 1. Introduction Many statistical applications involve the choice of an optimal model from a set of candidate models. To serve such a need, an extensive range of model selection techniques has been proposed in the literature, e.g. AIC (Akaike, 1973) and BIC (Schwarz, 1978). Traditional statistical inference is performed conditional on the selected “optimal” model. However, model selection methods are data-driven and
Psychometrika – Springer Journals
Published: Jun 4, 2018
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