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Bayesian Diagnostics of Structural E...

Chen, Ji.

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Bayesian Diagnostics of Structural Equation Models.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian Diagnostics of Structural Equation Models./
Author:	Chen, Ji.
Description:	167 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
Contained By:	Dissertation Abstracts International75-05B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3578882
ISBN:	9781303737701

Bayesian Diagnostics of Structural Equation Models.
Chen, Ji.

Bayesian Diagnostics of Structural Equation Models. - 167 p.

Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2013.

In the behavioral, social, psychological, and medical sciences, the most widely used models in assessing latent variables are structural equation models (SEMs). This thesis aims to develop Bayesian diagnostic procedures for basic and advanced SEMs such as nonlinear SEMs, transformation SEMs, two-level SEMs, and mixture SEMs. The first- and second-order local inference measures with the objective functions defined based on the logarithm of Bayes factor are proposed to perform the Bayesian diagnostics. Markov chain Monte Carlo (MCMC) methods, along with data augmentation, are developed to compute the local influence measures and to estimate unknown model parameters. Compared with conventional maximum likelihood-based diagnostic procedures, the proposed Bayesian diagnostic approach can not only detect outliers or influential points in the observed data, but also conduct model comparison and sensitivity analysis by perturbing the data, sampling distributions, and the prior distributions of model parameters via a variety of perturbations. The empirical performances of the proposed Bayesian diagnostic procedures are revealed through extensive simulation studies. Several real-life data sets are used to illustrate the application of our proposed methodology in the context of different SEMs.

ISBN: 9781303737701Subjects--Topical Terms:

517247
Statistics.

Bayesian Diagnostics of Structural Equation Models.
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245 1 0 $a Bayesian Diagnostics of Structural Equation Models.
300 $a 167 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-05(E), Section: B.
500 $a Adviser: Xinyuan Song.
502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2013.
520 $a In the behavioral, social, psychological, and medical sciences, the most widely used models in assessing latent variables are structural equation models (SEMs). This thesis aims to develop Bayesian diagnostic procedures for basic and advanced SEMs such as nonlinear SEMs, transformation SEMs, two-level SEMs, and mixture SEMs. The first- and second-order local inference measures with the objective functions defined based on the logarithm of Bayes factor are proposed to perform the Bayesian diagnostics. Markov chain Monte Carlo (MCMC) methods, along with data augmentation, are developed to compute the local influence measures and to estimate unknown model parameters. Compared with conventional maximum likelihood-based diagnostic procedures, the proposed Bayesian diagnostic approach can not only detect outliers or influential points in the observed data, but also conduct model comparison and sensitivity analysis by perturbing the data, sampling distributions, and the prior distributions of model parameters via a variety of perturbations. The empirical performances of the proposed Bayesian diagnostic procedures are revealed through extensive simulation studies. Several real-life data sets are used to illustrate the application of our proposed methodology in the context of different SEMs.
590 $a School code: 1307.
650 4 $a Statistics. $3 517247
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710 2 $a The Chinese University of Hong Kong (Hong Kong). $b Statistics. $3 2105458
773 0 $t Dissertation Abstracts International $g 75-05B(E).
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791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3578882