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Dynamic Structural Equation Modeling with Gaussian Processes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dynamic Structural Equation Modeling with Gaussian Processes./
Author:	Ziedzor, Reginald.
Description:	1 online resource (127 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
Subject:	Social sciences education. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29064764click for full text (PQDT)
ISBN:	9798819388464

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

Dynamic Structural Equation Modeling with Gaussian Processes. - 1 online resource (127 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798819388464Subjects--Topical Terms:

2144735
Social sciences education.
Subjects--Index Terms:

Bayesian analysisIndex Terms--Genre/Form:

542853
Electronic books.

Dynamic Structural Equation Modeling with Gaussian Processes.
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500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Koran, Jennifer.
502 $a Thesis (Ph.D.)--Southern Illinois University at Carbondale, 2022.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Social sciences education. $3 2144735
650 4 $a Social studies education. $3 3422293
650 4 $a Educational psychology. $3 517650
650 4 $a Quantitative psychology. $3 2144748
653 $a Bayesian analysis
653 $a Dynamic structural equation modeling
653 $a Gaussian process
653 $a Intensive longitudinal data
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