| 作者简介：Sik-Yum Lee is a professor of statistics at the Chinese University of Hong Kong. He earned his Ph.D. in biostatistics at the University of California, Los Angeles, USA. He received a distinguished service award from the International Chinese Statistical Association, is a former president of the Hong Kong Statistical Society, and is an elected member of the International Statistical Institute and a Fellow of the American Statistical Association. He serves as Associate Editor for Psychometrika and Computational Statistics & Data Analysis, and as a member of the Editorial Board of British Journal of Mathematical and Statistical Psychology, Structural Equation Modeling, Handbook of Computing and Statistics with Applications and Chinese Journal of Medicine. his research interests are in structural equation models, latent variable models, Bayesian methods and statistical diagnostics. he is editor of Handbook of Latent Variable and Related Models and author of over 140 papers. |
| About the Author Preface 1 Introduction 1.1 Standard Structural Equation Models 1.2 Covariance Structure Analysis 1.3 Why a New Book? 1.4 Objectives of the Book 1.5 Data Sets and Notations Appendix 1.1 References 2 Some Basic Structural Equation Models 2.1 Introduction 2.2 Exploratory Factor Analysis 2.3 Confirmatory and Higher-order Factor Aalalysis Models 2.4 The LISREL Model 2.5 The Bentler-Weeks Model 2.6 Discussion References 3 Covariance Structure Analysis 3.1 Introduction 3.2 Definitions, Notations and Preliminary Results 3.3 GLS Analysis of Covariance Structure 3.4 ML Analysis of Covariance Structure 3.5 Asymptotically Distribution-free Methods 3.6 Some Iterative Procedures Appendix 3.1: Matrix Calculus Appendix 3.2: Some Basic Results in Probability Theory Appendix 3.3: Proofs of Some Results References Bayesian Estimation of Structural Equation Models 4.1 Introduction 4.2 Basic Principlesand Concepts of Bayesian Analysis of SEMs 4.3 Bayesian Estimation of the CFA Model 4.4 Bayesian Estimation of Standard SEMs 4.5 Bayesian Estimation via WinBUGS Appendix 4.1 : The Metropolis-Hastings Algorithm Appendix 4.2: EPSR Value Appendix 4.3: Derivations of Conditional Distributions References Model Comparison and Model Checking 5.1 Introduction 5.2 Bayes Factor 5.3 Path Sampling 5.4 An Application: Bayesian Analysis of SEMs with Fixed Covariates 5.5 Other Methods 5.6 Discussion Appendix 5.1 : Another Proof of Equation (5.10) Appendix 5.2: Conditional Distributions for Simulating (0, ~|Y, t) Appendix 5.3: PP p-values for Model Assessment Rerences 6 Structural Equation Models with Continuous and Ordered Categorical Variables 6.1 Introduction 6.2 The Basic Model 6.3 Bayesian Estimation and Goodness-of-fit 6.4 Bayesian Model Comparison 6.5 Application 1: Bayesian Selection of the Number of Factors in EFA 6.6 Application 2: Bayesian Analysis of Quality of Life Data References 7 Structural Equation Models with Dichotomous Variables 7.1 Introduction 7.2 Bayesian Analysis 7.3 Analysis of a Multivariate Probit Confirmatory Factor Analysis Model …… 8 Nenlinear Structural Equation Models 9 Two-Level Nonlinear Structural Equation Models 10 Multisample Analysis of Structural Equation Models 11 Finite Mixtures in Structur Equation Models 12 Structural Equation Models with Missing Data 13 Structural Equation Models with Exponential Family of Distributions 14 Conclusion Index |
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