发展了新的模型和统计方法以更精确地分析更加复杂的数据。结构方

程模型的贝叶斯方法使用先验信息，得到更准确的参数估计、潜在变量估

计以及用于模型比较的统计量，并且在小样本情况下能得到更稳健的结果

。

香港中文大学统计系李锡钦讲座教授的专著《结构方程模型——贝叶

斯方法》概括了本学科的近期发展，并有如下特点：示范如何使用强大的

统计计算工具得到贝叶斯结果；讨论用于模型比较的贝叶斯因子和偏差信

息准则；涵盖多种复杂的模型；通过模拟研究以及来自工商管理学、教育

学、心理学、公共卫生和社会学的实际数据说明所提出的方法；通过辅助

网页提供的程序代码以及数据集示范免费软件WinBUGS的应用。

《结构方程模型——贝叶斯方法》可作为不同领域(包括统计学、生物

统计学、商学、教育学、医学、心理学、公共卫生与社会学等)的教师、学

生和研究人员学习统计分析、统计方法的工具书。 Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances. Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results. Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison. Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations. Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology. Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets. Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.

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结构方程模型:贝叶斯方法