TY - JOUR
T1 - Out-of-bag Prediction Error
T2 - A Cross Validation Index for Generalized Structured Component Analysis
AU - Cho, Gyeongcheol
AU - Jung, Kwanghee
AU - Hwang, Heungsun
N1 - Publisher Copyright:
© 2019, © 2019 Taylor & Francis Group, LLC.
PY - 2019/7/4
Y1 - 2019/7/4
N2 - 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.
AB - 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.
KW - Structural equation modeling
KW - cross validation
KW - generalized structural component analysis
KW - model selection
KW - overall model fit
KW - predictability
UR - http://www.scopus.com/inward/record.url?scp=85064506969&partnerID=8YFLogxK
U2 - 10.1080/00273171.2018.1540340
DO - 10.1080/00273171.2018.1540340
M3 - Article
C2 - 30977677
AN - SCOPUS:85064506969
SN - 0027-3171
VL - 54
SP - 505
EP - 513
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
IS - 4
ER -