Semiparametric Bayesian analysis of structural equation models with fixed covariates
Publication in refereed journal


摘要Latent variables play the most important role in structural equation modeling. In almost all existing structural equation models (SEMs), it is assumed that the distribution of the latent variables is normal. As this assumption is likely to be violated in many biomedical researches, a semiparametric Bayesian approach for relaxing it is developed in this paper. In the context of SEMs with covariates, we provide a general Bayesian framework in which a semiparametric hierarchical modeling with an approximate truncation Dirichlet process prior distribution is specified for the latent variables. The stick-breaking prior and the blocked Gibbs sampler are used for efficient simulation in the posterior analysis. The developed methodology is applied to a study of kidney disease in diabetes patients. A simulation study is conducted to reveal the empirical performance of the proposed approach. Supplementary electronic material for this paper is available in Wiley InterScience at Copyright (C) 2007 John Wiley & Sons, Ltd.
著者Lee SY, Lu B, Song XY
期刊名稱Statistics in Medicine
出版社Wiley: 12 months
頁次2341 - 2360
關鍵詞blocked Gibbs sampler; Dirichlet process; latent variables; semiparametric hierarchical modeling; stick-breaking prior
Web of Science 學科類別Mathematical & Computational Biology; MATHEMATICAL & COMPUTATIONAL BIOLOGY; Mathematics; Medical Informatics; MEDICAL INFORMATICS; Medicine, Research & Experimental; MEDICINE, RESEARCH & EXPERIMENTAL; Public, Environmental & Occupational Health; PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH, SCI; Research & Experimental Medicine; Statistics & Probability; STATISTICS & PROBABILITY

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