Bayesian model comparison of nonlinear structural equation models with missing continuous and ordinal categorical data
Publication in refereed journal


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摘要Missing data are very common in behavioural and psychological research. In this paper, we develop a Bayesian approach in the context of a general nonlinear structural equation model with missing continuous and ordinal categorical data. In the development, the missing data are treated as latent quantities, and provision for the incompleteness of the data is made by a hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm. We show by means of a simulation study that the Bayesian estimates are accurate. A Bayesian model comparison procedure based on the Bayes factor and path sampling is proposed. The required observations from the posterior distribution for computing the Bayes factor are simulated by the hybrid algorithm in Bayesian estimation. Our simulation results indicate that the correct model is selected more frequently when the incomplete records are used in the analysis than when they are ignored. the methodology is further illustrated with a real data set from a study concerned with an AIDS preventative intervention for Filipina sex workers.
著者Lee SY, Song XY
期刊名稱British Journal of Mathematical and Statistical Psychology
出版年份2004
月份5
日期1
卷號57
出版社BRITISH PSYCHOLOGICAL SOC
頁次131 - 150
國際標準期刊號0007-1102
電子國際標準期刊號2044-8317
語言英式英語
Web of Science 學科類別Mathematics; Mathematics, Interdisciplinary Applications; MATHEMATICS, INTERDISCIPLINARY APPLICATIONS; Psychology; Psychology, Experimental; PSYCHOLOGY, EXPERIMENTAL; Psychology, Mathematical; PSYCHOLOGY, MATHEMATICAL; Statistics & Probability; STATISTICS & PROBABILITY

上次更新時間 2022-11-01 於 23:21