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Dynamic Structural Equation Modeling with Gaussian Processes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamic Structural Equation Modeling with Gaussian Processes.
作者:	Ziedzor, Reginald.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2022
面頁冊數:	127 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
附註:	Advisor: Koran, Jennifer.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Social sciences education.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29064764
ISBN:	9798819388464

Dynamic Structural Equation Modeling with Gaussian Processes.
Ziedzor, Reginald.

Dynamic Structural Equation Modeling with Gaussian Processes. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 127 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Southern Illinois University at Carbondale, 2022.

This item must not be sold to any third party vendors.

The dynamic structural equation modeling (DSEM) framework incorporates hierarchical latent modeling (HLM), structural equation modeling (SEM), time series analysis (TSA), and time-varying effects modeling (TVEM) to model the dynamic relationship between latent and observed variables. To model the functional relationships between variables, a Gaussian process (GP), by definition of its covariance function(s), allows researchers to define Gaussian distributions over functions of input variables. Therefore, by incorporating GPs to model the presence of significant trend in either latent or observed variables, this dissertation explores the adequacy and performance of GPs in manipulated conditions of sample size using the flexible Bayesian analysis approach. The overall results of these Monte Carlo simulation studies showcase the ability of the multi-output GPs to properly explore the presence of trends. Also, in modeling intensive longitudinal data, GPs can be specified to properly account for trends, without generating significantly biased and imprecise estimates.

ISBN: 9798819388464Subjects--Topical Terms:

942384
Social sciences education.
Subjects--Index Terms:

Bayesian analysis

Dynamic Structural Equation Modeling with Gaussian Processes.
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502 $a Thesis (Ph.D.)--Southern Illinois University at Carbondale, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a The dynamic structural equation modeling (DSEM) framework incorporates hierarchical latent modeling (HLM), structural equation modeling (SEM), time series analysis (TSA), and time-varying effects modeling (TVEM) to model the dynamic relationship between latent and observed variables. To model the functional relationships between variables, a Gaussian process (GP), by definition of its covariance function(s), allows researchers to define Gaussian distributions over functions of input variables. Therefore, by incorporating GPs to model the presence of significant trend in either latent or observed variables, this dissertation explores the adequacy and performance of GPs in manipulated conditions of sample size using the flexible Bayesian analysis approach. The overall results of these Monte Carlo simulation studies showcase the ability of the multi-output GPs to properly explore the presence of trends. Also, in modeling intensive longitudinal data, GPs can be specified to properly account for trends, without generating significantly biased and imprecise estimates.
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653 $a Gaussian process
653 $a Intensive longitudinal data
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