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Bibliographies thématiques / Dynamic structural equation models

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Littérature scientifique sur le sujet « Dynamic structural equation models »

Auteur : Grafiati

Publié le 10 mars 2023

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Thèses sur le sujet "Dynamic structural equation models"

1

Ciraki, Dario. "Dynamic structural equation models : estimation and interference." Thesis, London School of Economics and Political Science (University of London), 2007. http://etheses.lse.ac.uk/2937/.

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The thesis focuses on estimation of dynamic structural equation models in which some or all variables might be unobservable (latent) or measured with error. Moreover, we consider the situation where latent variables can be measured with multiple observable indicators and where lagged values of latent variables might be included in the model. This situation leads to a dynamic structural equation model (DSEM), which can be viewed as dynamic generalisation of the structural equation model (SEM). Taking the mismeasurement problem into account aims at reducing or eliminating the errors-in-variables

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Jung, Kwang Hee. "Dynamic GSCA generalized structured component analysis: a structural equation model for analyzing effective connectivity in functional neuroimaging." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106488.

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Structural equation modeling (SEM) is often used to investigate effective connectivity in functional neuroimaging studies. Modeling effective connectivity refers to an approach in which a number of specific brain regions, called regions of interest (ROIs), are selected according to some prior knowledge about the regions, and directional (causal) relationships between them are hypothesized and tested. Existing methods for SEM, however, have serious limitations in terms of their computational capacity and the range of models that can be specified. To alleviate these difficulties, I propose a new

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Yang, Yang. "Two-dimensional dynamic analysis of functionally graded structures by using meshfree boundary-domain integral equation method." Thesis, University of Macau, 2015. http://umaclib3.umac.mo/record=b3335354.

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Zhou, Lixing. "Dynamic generalized (multiple-set) structured canonical correlation analysis (dynamic GCANO): a structural equation model for simultaneous analysis of multiple-subject effective connectivity in functional neuroimaging studies." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123190.

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Effective connectivity in functional neuroimaging studies is defined as the time dependent causal influence that a certain brain region of interest (ROI) exerts on another. Structural equation modeling (SEM) is regularly employed to analyze effective connectivity. In recent years, various SEM methods have been proposed to model effective connectivity. However, there has been little attempt to develop SEM methods for analyzing common patterns of effective connectivity across subjects despite the prevalence of multiple-subject research in effective connectivity. This dissertation proposes a meth

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Hu, Shanshan. "AFFECT, MOTIVATION, AND ENGAGEMENT IN THE CONTEXT OF MATHEMATICS EDUCATION: TESTING A DYNAMIC MODEL OF INTERACTIVE RELATIONSHIPS." UKnowledge, 2018. https://uknowledge.uky.edu/edp_etds/71.

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The present study tested the interactive model of affect, motivation, and engagement (Linnenbrink, 2007) in mathematics education with a nationally representative sample. Self-efficacy, self-concept, and anxiety were indicators of pleasant and unpleasant affect. Intrinsic and extrinsic motivation were indicators of mastery and performance approach. Persistence and cognitive activation were indicators of behavioral and cognitive engagement. The 2012 Programme for International Student Assessment (PISA) supplied a sample of 4,978 students from the United States for structural equation modeling.

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Han, Sukho Brown D. Scott. "The impact analysis of structural change in Korean agriculture with respect to the Korean-United States free trade agreement dynamic simultaneous equation model approach /." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6969.

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Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 26, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Scott Brown. Includes bibliographical references.

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Bocaccio, Alessandro Antunes. "A inteligência como capacidade dinâmica : uma relação entre processo de monitoramento de ambiente externo e vantagem competitiva." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/163858.

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As organizações estão expostas a uma quantidade e variabilidade cada vez mais crescente de informações. A capacidade de se antecipar às tendências e se adequar ao ambiente passa a ser, além de fonte de vantagem competitiva, fator necessário para a sobrevivência. Nessa realidade, organizações frequentemente apresentam dificuldades de leitura de seu ambiente e adaptação ao meio. Acredita-se na necessidade de desenvolvimento de uma capacidade interna da organização para que o monitoramento do ambiente se estabeleça, bem como análise de oportunidades, planejamento de ações de melhoria e reconfigur

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Konarski, Roman. "Sensitivity analysis for structural equation models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22893.pdf.

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Cerqueira, Pedro Henrique Ramos. "Structural equation models applied to quantitative genetics." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-05112015-145419/.

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Causal models have been used in different areas of knowledge in order to comprehend the causal associations between variables. Over the past decades, the amount of studies using these models have been growing a lot, especially those related to biological systems where studying and learning causal relationships among traits are essential for predicting the consequences of interventions in such system. Graph analysis (GA) and structural equation modeling (SEM) are tools used to explore such associations. While GA allows searching causal structures that express qualitatively how variables are cau

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Jung, Sunho. "Regularized structural equation models with latent variables." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66858.

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In structural equation models with latent variables, maximum likelihood (ML) estimation is currently the most prevailing estimation method. However, the ML method fails to provide accurate solutions in a number of situations including those involving small sample sizes, nonnormality, and model misspecification. To over come these difficulties, regularized extensions of two-stage least squares estimation are proposed that incorporate a ridge type of regularization in the estimation of parameters. Two simulation studies and two empirical applications demonstrate that the propos

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