CUHK electronic theses & dissertations collection

Keywords: Latent variables, Ordered categorical data, Unordered categorical data, Nonignorable missing data, Maximum likelihood approach, Bayesian approach.Structural equation models (SEMs) have been widely applied to examine interrelationships among latent and observed variables in behavioral, psychological, and medical research. Motivated by the fact that correlated ordered and unordered categorical variables are frequently encountered in practical applications, a nonlinear SEM that accommodates fixed covariates, mixed continuous, ordered categorical, and unordered categorical variables is proposed. Maximum likelihood methods for estimation and model comparison are discussed. Besides, missing data are frequently encountered in practical researches; a lot of attention has been devoted to analyze various SEMs with missing data. Bayesian analysis, including parameter estimate and model comparison, of a nonlinear SEM with mixed continuous, ordered and unordered categorical variables, and nonignorable missing entries is also considered in the thesis. Simulation studies are conducted to reveal the performance of the proposed methods. Moreover, we apply our methodologies to analyze the real-life data set about cardiovascular disease. As none of the existing SEMs can simultaneously accommodate fixed covariates, mixed continuous, ordered and unordered categorical data, and missing data, this thesis offers a novel SEM to cope with more complex practical problems and develop maximum likelihood and Bayesian methods for obtaining results in estimation and model comparison.Cai, Jingheng.Advisers: Sik-Yum Lee; Xin-Yuan Song.Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3584.Thesis (Ph.D.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 76-82).Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.Abstracts in English and Chinese.School code: 1307