by Jian-Qing Shi. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 118-123). / Chapter Chapter 1. --- Introduction and overview --- p.1 / Chapter Chapter 2. --- General methodology --- p.8 / Chapter Chapter 3. --- A Bayesian approach to confirmatory factor analysis --- p.16 / Chapter 3.1 --- Confirmatory factor analysis model and its prior --- p.16 / Chapter 3.2 --- The algorithm of data augmentation --- p.19 / Chapter 3.2.1 --- Data augmentation and one-run method --- p.19 / Chapter 3.2.2 --- Rao-Blackwellized estimation --- p.22 / Chapter 3.3 --- Asymptotic properties --- p.28 / Chapter 3.3.1 --- Asymptotic normality and posterior covariance matrix --- p.28 / Chapter 3.3.2 --- Goodness-of-fit statistic --- p.31 / Chapter Chapter 4. --- Bayesian inference for structural equation models --- p.34 / Chapter 4.1 --- LISREL Model and prior information --- p.34 / Chapter 4.2 --- Algorithm and conditional distributions --- p.38 / Chapter 4.2.1 --- Data augmentation algorithm --- p.38 / Chapter 4.2.2 --- Conditional distributions --- p.39 / Chapter 4.3 --- Posterior analysis --- p.44 / Chapter 4.3.1 --- Rao-Blackwellized estimation --- p.44 / Chapter 4.3.2 --- Asymptotic properties and goodness-of-fit statistic --- p.45 / Chapter 4.4 --- Simulation study --- p.47 / Chapter Chapter 5. --- A Bayesian estimation of factor score with non-standard data --- p.52 / Chapter 5.1 --- General Bayesian approach to polytomous data --- p.52 / Chapter 5.2 --- Covariance matrix of the posterior distribution --- p.61 / Chapter 5.3 --- Data augmentation --- p.65 / Chapter 5.4 --- EM algorithm --- p.68 / Chapter 5.5 --- Analysis of censored data --- p.72 / Chapter 5.5.1 --- General Bayesian approach --- p.72 / Chapter 5.5.2 --- EM algorithm --- p.76 / Chapter 5.6 --- Analysis of truncated data --- p.78 / Chapter Chapter 6. --- Structural equation model with continuous and polytomous data --- p.82 / Chapter 6.1 --- Factor analysis model with continuous and polytomous data --- p.83 / Chapter 6.1.1 --- Model and Bayesian inference --- p.83 / Chapter 6.1.2 --- Gibbs sampler algorithm --- p.85 / Chapter 6.1.3 --- Thresholds parameters --- p.89 / Chapter 6.1.4 --- Posterior analysis --- p.92 / Chapter 6.2 --- LISREL model with continuous and polytomous data --- p.94 / Chapter 6.2.1 --- LISREL model and Bayesian inference --- p.94 / Chapter 6.2.2 --- Posterior analysis --- p.101 / Chapter 6.3 --- Simulation study --- p.103 / Chapter Chapter 7. --- Further development --- p.108 / Chapter 7.1 --- More about one-run method --- p.108 / Chapter 7.2 --- Structural equation model with censored data --- p.111 / Chapter 7.3 --- Multilevel structural equation model --- p.114 / References --- p.118 / Appendix --- p.124 / Chapter A.1 --- The derivation of conditional distribution --- p.124 / Chapter A.2 --- Generate a random variate from normal density which restricted in an interval --- p.129 / Tables --- p.132 / Figures --- p.155
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_321550 |
| Date | January 1996 |
| Contributors | Shi, Jianqing., Chinese University of Hong Kong Graduate School. Division of Statistics. |
| Publisher | Chinese University of Hong Kong |
| Source Sets | The Chinese University of Hong Kong |
| Language | English |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, iii, 154, [28] leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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