Bayesian analysis of structural equation models with nonlinear covariates and latent variables
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摘要In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the proposed model. Markov chain Monte Carlo methods for obtaining Bayesian estimates and their standard error estimates, highest posterior density intervals, and a PP p value are developed. Results obtained from two simulation studies are reported to respectively reveal the empirical performance of the proposed Bayesian estimation in analyzing complex nonlinear SEMs, and in analyzing nonlinear SEMs with the normal assumption of the exogenous latent variables violated. The proposed methodology is further illustrated by a real example. Detailed interpretation about the interaction terms is presented.
著者Song XY, Lee SY
期刊名稱Multivariate Behavioral Research
出版年份2006
月份1
日期1
卷號41
期次3
出版社LAWRENCE ERLBAUM ASSOC INC
頁次337 - 365
國際標準期刊號0027-3171
語言英式英語
Web of Science 學科類別Mathematical Methods In Social Sciences; Mathematics; Mathematics, Interdisciplinary Applications; MATHEMATICS, INTERDISCIPLINARY APPLICATIONS; Psychology; Psychology, Experimental; PSYCHOLOGY, EXPERIMENTAL; Social Sciences, Mathematical Methods; SOCIAL SCIENCES, MATHEMATICAL METHODS; Statistics & Probability; STATISTICS & PROBABILITY

上次更新時間 2022-11-01 於 23:57