發展瞭新的模型和統計方法以更精確地分析更加復雜的數據。結構方

程模型的貝葉斯方法使用先驗信息，得到更準確的參數估計、潛在變量估

計以及用於模型比較的統計量，並且在小樣本情況下能得到更穩健的結果

。

香港中文大學統計係李锡欽講座教授的專著《結構方程模型——貝葉

斯方法》概括瞭本學科的近期發展，並有如下特點：示範如何使用強大的

統計計算工具得到貝葉斯結果；討論用於模型比較的貝葉斯因子和偏差信

息準則；涵蓋多種復雜的模型；通過模擬研究以及來自工商管理學、教育

學、心理學、公共衛生和社會學的實際數據說明所提齣的方法；通過輔助

網頁提供的程序代碼以及數據集示範免費軟件WinBUGS的應用。

《結構方程模型——貝葉斯方法》可作為不同領域(包括統計學、生物

統計學、商學、教育學、醫學、心理學、公共衛生與社會學等)的教師、學

生和研究人員學習統計分析、統計方法的工具書。 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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結構方程模型:貝葉斯方法