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• W2003058911 abstract "Abstract Recently, it is recognized that nonlinear relationships among latent variables in a structural equation model are important. In this article, maximum likelihood (ML) analysis of a general nonlinear structural equation model that contains fixed covariates in the measurement equation and the nonlinear structural equation is investigated. A MCEM algorithm is implemented to obtain the ML estimates, in which the E-step is completed with the help of a hybrid algorithm that combines the Gibbs sampler and the Metropolis–Hastings algorithm whilst the M-step is completed by conditional maximization. The importance sampling is employed to compute the observed-data likelihood in the Bayesian Information Criterion for model comparison. The methodology is illustrated with a simulation study and a real example." @default.
• W2003058911 created "2016-06-24" @default.
• W2003058911 creator A5056116087 @default.
• W2003058911 creator A5082070432 @default.
• W2003058911 date "2003-10-01" @default.
• W2003058911 modified "2023-10-16" @default.
• W2003058911 title "Maximum likelihood estimation and model comparison of nonlinear structural equation models with continuous and polytomous variables" @default.
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• W2003058911 doi "https://doi.org/10.1016/s0167-9473(02)00305-5" @default.
• W2003058911 hasPublicationYear "2003" @default.
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