國立高雄大學圖資館 |

Language: English

Back

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.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2022
Description:	127 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Notes:	Advisor: Koran, Jennifer.
Contained By:	Dissertations Abstracts International83-12B.
Subject:	Social sciences education.
Online resource:	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.
LDR:02303nmm a2200385 4500 001 636090
005 20230501063856.5
006 m o d
007 cr#unu||||||||
008 230724s2022 ||||||||||||||||| ||eng d
020 $a 9798819388464
035 $a (MiAaPQ)AAI29064764
035 $a AAI29064764
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ziedzor, Reginald. $3 942382
245 1 0 $a Dynamic Structural Equation Modeling with Gaussian Processes.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 127 p.
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.
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.
590 $a School code: 0209.
650 4 $a Social sciences education. $3 942384
650 4 $a Social studies education. $3 942385
650 4 $a Educational psychology. $3 181151
650 4 $a Quantitative psychology. $3 708538
653 $a Bayesian analysis
653 $a Dynamic structural equation modeling
653 $a Gaussian process
653 $a Intensive longitudinal data
690 $a 0534
690 $a 0632
690 $a 0525
710 2 $a Southern Illinois University at Carbondale. $b Quantitative Methods. $3 942383
773 0 $t Dissertations Abstracts International $g 83-12B.
790 $a 0209
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29064764