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Frequentist Model Averaging in Structural Equation Modelling
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0003-4415-8734
2019 (English)In: Psychometrika, ISSN 0033-3123, E-ISSN 1860-0980, Vol. 84, no 1, p. 84-104Article in journal (Refereed) Published
Abstract [en]

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 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.

Place, publisher, year, edition, pages
SPRINGER , 2019. Vol. 84, no 1, p. 84-104
Keywords [en]
model selection, post-selection inference, coverage probability, local asymptotic, goodness-of-fit
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-378366DOI: 10.1007/s11336-018-9624-yISI: 000458464200005PubMedID: 29869128OAI: oai:DiVA.org:uu-378366DiVA, id: diva2:1293914
Funder
Swedish Research Council, 2017-01175Available from: 2019-03-05 Created: 2019-03-05 Last updated: 2019-03-05Bibliographically approved

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Jin, ShaoboAnkargren, Sebastian

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