Expected predictive least squares for model selection in covariance structures

JOURNAL OF MULTIVARIATE ANALYSIS

卷:155

Article

Ogasawara, Haruhiko¹ 

[1] Otaru Univ, Dept Informat & Management Sci, 3-5-21 Midori, Otaru, Hokkaido 0478501, Japan

关键词: Asymptotic bias;    Model fit;    Factor analysis;    Generalized least squares;    AIC;   

DOI  :  10.1016/j.jmva.2016.12.007

来源: Elsevier

【 摘 要 】

Predictive least squares (PLS) using future data to be predicted by current data are defined in covariance structure analysis. The expected predictive least squares (EPLS) obtained by two-fold expectation of PLS are unknown fit indexes. Using the asymptotic biases of weighted least squares given by current data for estimation of EPLS in covariance structures, corrected least square criteria derived similarly to the Takeuchi information criterion are shown to be asymptotically unbiased under arbitrary distributions. Simulations for model selection in exploratory factor analysis show improvements over typical current fit indexes as RMSEA and AIC. (C) 2016 Elsevier Inc. All rights reserved.

【 授权许可】

Free