Frequentist Model Averaging in Structure Equation Model With Ordinal Data.

ABSTRACT: In practice, it is common that a best fitting structural equation model (SEM) is selected from a set of candidate SEMs and inference is conducted conditional on the selected model. Such post-selection inference ignores the model selection uncertainty and yields too optimistic inference. Using the largest candidate model avoids model selection uncertainty but introduces a large variation. Jin and Ankargren (Psychometrika 84:84-104, 2019) proposed to use frequentist model averaging in SEM with continuous data as a compromise between model selection and the full model. They assumed that the true values of the parameters depend on [Formula: see text] with n being the sample size, which is known as a local asymptotic framework. This paper shows that their results are not directly applicable to

SUBMITTER: Jin S 

PROVIDER: S-EPMC9433363 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature