Bayesian analysis of nonlinear structural equation models with nonignorable missing data
Publication in refereed journal

香港中文大學研究人員

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摘要A Bayesian approach is developed for analyzing nonlinear structural equation models with nonignorable missing data. The nonignorable missingness mechanism is specified by a logistic regression model. A hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm is used to produce the joint Bayesian estimates of structural parameters, latent variables, parameters in the nonignorable missing model, as well as their standard errors estimates. A goodness-of-fit statistic for assessing the plausibility of the posited nonlinear structural equation model is introduced, and a procedure for computing the Bayes factor for model comparison is developed via path sampling. Results obtained with respect to different missing data models, and different prior inputs are compared via simulation studies. In particular, it is shown that in the presence of nonignorable missing data, results obtained by the proposed method with a nonignorable missing data model are significantly better than those that are obtained under the missing at random assumption. A real example is presented to illustrate the newly developed Bayesian methodologies.
著者Lee SY, Tang NS
期刊名稱Psychometrika
出版年份2006
月份9
日期1
卷號71
期次3
出版社SPRINGER
頁次541 - 564
國際標準期刊號0033-3123
電子國際標準期刊號1860-0980
語言英式英語
關鍵詞Bayes factor; Gibbs sampler; Metropolis-Hastings algorithm; model comparison; nonignorable missing data; path sampling
Web of Science 學科類別Mathematical Methods In Social Sciences; Mathematics; Mathematics, Interdisciplinary Applications; MATHEMATICS, INTERDISCIPLINARY APPLICATIONS; Psychology; Psychology, Mathematical; PSYCHOLOGY, MATHEMATICAL; Social Sciences, Mathematical Methods; SOCIAL SCIENCES, MATHEMATICAL METHODS

上次更新時間 2022-10-01 於 23:55