Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis

Gyeongcheol Cho, Kwanghee Jung, Heungsun Hwang

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.

Original languageEnglish
Pages (from-to)505-513
Number of pages9
JournalMultivariate Behavioral Research
Issue number4
StatePublished - Jul 4 2019


  • Structural equation modeling
  • cross validation
  • generalized structural component analysis
  • model selection
  • overall model fit
  • predictability


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