Relevant bibliographies by topics / Bayesian structural equation model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian structural equation model'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Bayesian structural equation model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Bayesian structural equation model"

1

Lee, Sik-Yum, and Xin-Yuan Song. "Bayesian structural equation model." Wiley Interdisciplinary Reviews: Computational Statistics 6, no. 4 (June 16, 2014): 276–87. http://dx.doi.org/10.1002/wics.1311.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Wang, Yifan, Xiang-Nan Feng, and Xin-Yuan Song. "Bayesian Quantile Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 23, no. 2 (July 25, 2015): 246–58. http://dx.doi.org/10.1080/10705511.2015.1033057.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Song, Xin-Yuan, Ye-Mao Xia, Jun-Hao Pan, and Sik-Yum Lee. "Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 18, no. 1 (January 13, 2011): 55–72. http://dx.doi.org/10.1080/10705511.2011.532720.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Feng, Xiang-Nan, Yifan Wang, Bin Lu, and Xin-Yuan Song. "Bayesian regularized quantile structural equation models." Journal of Multivariate Analysis 154 (February 2017): 234–48. http://dx.doi.org/10.1016/j.jmva.2016.11.002.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Stenling, Andreas, Andreas Ivarsson, Urban Johnson, and Magnus Lindwall. "Bayesian Structural Equation Modeling in Sport and Exercise Psychology." Journal of Sport and Exercise Psychology 37, no. 4 (August 2015): 410–20. http://dx.doi.org/10.1123/jsep.2014-0330.

Full text
Abstract:
Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.
APA, Harvard, Vancouver, ISO, and other styles
6

Jiang, Xiaomo, and Sankaran Mahadevan. "Bayesian structural equation modeling method for hierarchical model validation." Reliability Engineering & System Safety 94, no. 4 (April 2009): 796–809. http://dx.doi.org/10.1016/j.ress.2008.08.008.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Levy, Roy. "Bayesian Data-Model Fit Assessment for Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 18, no. 4 (October 5, 2011): 663–85. http://dx.doi.org/10.1080/10705511.2011.607723.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lee, Sik-Yum, and Jian-Qing Shi. "Bayesian Analysis of Structural Equation Model With Fixed Covariates." Structural Equation Modeling: A Multidisciplinary Journal 7, no. 3 (July 2000): 411–30. http://dx.doi.org/10.1207/s15328007sem0703_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Guo, Ruixin, Hongtu Zhu, Sy-Miin Chow, and Joseph G. Ibrahim. "Bayesian Lasso for Semiparametric Structural Equation Models." Biometrics 68, no. 2 (February 29, 2012): 567–77. http://dx.doi.org/10.1111/j.1541-0420.2012.01751.x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Chen, Ji, Pengfei Liu, and Xinyuan Song. "Bayesian diagnostics of transformation structural equation models." Computational Statistics & Data Analysis 68 (December 2013): 111–28. http://dx.doi.org/10.1016/j.csda.2013.06.012.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Bayesian structural equation model"

1

Yoo, Keunyoung. "Probabilistic SEM : an augmentation to classical Structural equation modelling." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/66521.

Full text
Abstract:
Structural equation modelling (SEM) is carried out with the aim of testing hypotheses on the model of the researcher in a quantitative way, using the sampled data. Although SEM has developed in many aspects over the past few decades, there are still numerous advances which can make SEM an even more powerful technique. We propose representing the nal theoretical SEM by a Bayesian Network (BN), which we would like to call a Probabilistic Structural Equation Model (PSEM). With the PSEM, we can take things a step further and conduct inference by explicitly entering evidence into the network and performing di erent types of inferences. Because the direction of the inference is not an issue, various scenarios can be simulated using the BN. The augmentation of SEM with BN provides signi cant contributions to the eld. Firstly, structural learning can mine data for additional causal information which is not necessarily clear when hypothesising causality from theory. Secondly, the inference ability of the BN provides not only insight as mentioned before, but acts as an interactive tool as the `what-if' analysis is dynamic.
Mini Dissertation (MCom)--University of Pretoria, 2018.
Statistics
MCom
Unrestricted
APA, Harvard, Vancouver, ISO, and other styles
2

Pfleger, Phillip Isaac. "Exploring Fit for Nonlinear Structural Equation Models." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7370.

Full text
Abstract:
Fit indices and fit measures commonly used to determine the accuracy and desirability of structural equation models are expected to be insensitive to nonlinearity in the data. This includes measures as ubiquitous as the CFI, TLI, RMSEA, SRMR, AIC, and BIC. Despite this, some software will report these measures when certain models are used. Consequently, some researchers may be led to use these fit measures without realizing the impropriety of the act. Alternative fit measures have been proposed, but these measures require further testing. As part of this thesis, a large simulation study was carried out to investigate alternative fit measures and to confirm whether the traditional measures are practically blind to nonlinearity in the data. The results of the simulation provide conclusive evidence that fit statistics and fit indices based on the chi-square distribution or the residual covariance matrix are entirely insensitive to nonlinearity. The posterior predictive p-value was also insensitive to nonlinearity. Only fit measures based on the structural residuals (i.e., HFI and R-squared) showed any sensitivity to nonlinearity. Of these, the R-squared was the only reliable measure of nonlinear model misspecification. This thesis shows that an effective strategy for determining whether a nonlinear model is preferable to a linear one involves using the R-squared to compare models that have been fit to the same data. An R-squared that is much larger for the nonlinear model than the linear model suggests that the linear model may be less desirable than the nonlinear model. The proposed method is intended to be supplementary to substantive theory. It is argued that any dependence on fit indices or fit statistics that places these measures on a higher pedestal than substantive theory will invariably lead to blindness on the part of the researcher. In other words, unwavering adherence to goodness-of-fit measures limits the researcher<'>s vision to what the measures themselves can detect.
APA, Harvard, Vancouver, ISO, and other styles
3

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/.

Full text
Abstract:
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 causally connected, fitting SEM with a known causal structure allows to infer the magnitude of causal effects. Also SEM can be viewed as multiple regression models in which response variables can be explanatory variables for others. In quantitative genetics studies, SEM aimed to study the direct and indirect genetic effects associated to individuals through information related to them, beyond the observed characteristics, such as the kinship relations. In those studies typically the assumptions of linear relationships among traits are made. However, in some scenarios, nonlinear relationships can be observed, which make unsuitable the mentioned assumptions. To overcome this limitation, this paper proposes to use a mixed effects polynomial structural equation model, second or superior degree, to model those nonlinear relationships. Two studies were developed, a simulation and an application to real data. The first study involved simulation of 50 data sets, with a fully recursive causal structure involving three characteristics in which linear and nonlinear causal relations between them were allowed. The second study involved the analysis of traits related to dairy cows of the Holstein breed. Phenotypic relationships between traits were calving difficulty, gestation length and also the proportion of perionatal death. We compare the model of multiple traits and polynomials structural equations models, under different polynomials degrees in order to assess the benefits of the SEM polynomial of second or higher degree. For some situations the inappropriate assumption of linearity results in poor predictions of the direct, indirect and total of the genetic variances and covariance, either overestimating, underestimating, or even assign opposite signs to covariances. Therefore, we conclude that the inclusion of a polynomial degree increases the SEM expressive power.
Modelos causais têm sido muitos utilizados em estudos em diferentes áreas de conhecimento, a fim de compreender as associações ou relações causais entre variáveis. Durante as últimas décadas, o uso desses modelos têm crescido muito, especialmente estudos relacionados à sistemas biológicos, uma vez que compreender as relações entre características são essenciais para prever quais são as consequências de intervenções em tais sistemas. Análise do grafo (AG) e os modelos de equações estruturais (MEE) são utilizados como ferramentas para explorar essas relações. Enquanto AG nos permite buscar por estruturas causais, que representam qualitativamente como as variáveis são causalmente conectadas, ajustando o MEE com uma estrutura causal conhecida nos permite inferir a magnitude dos efeitos causais. Os MEE também podem ser vistos como modelos de regressão múltipla em que uma variável resposta pode ser vista como explanatória para uma outra característica. Estudos utilizando MEE em genética quantitativa visam estudar os efeitos genéticos diretos e indiretos associados aos indivíduos por meio de informações realcionadas aos indivíduas, além das característcas observadas, como por exemplo o parentesco entre eles. Neste contexto, é tipicamente adotada a suposição que as características observadas são relacionadas linearmente. No entanto, para alguns cenários, relações não lineares são observadas, o que torna as suposições mencionadas inadequadas. Para superar essa limitação, este trabalho propõe o uso de modelos de equações estruturais de efeitos polinomiais mistos, de segundo grau ou seperior, para modelar relações não lineares. Neste trabalho foram desenvolvidos dois estudos, um de simulação e uma aplicação a dados reais. O primeiro estudo envolveu a simulação de 50 conjuntos de dados, com uma estrutura causal completamente recursiva, envolvendo 3 características, em que foram permitidas relações causais lineares e não lineares entre as mesmas. O segundo estudo envolveu a análise de características relacionadas ao gado leiteiro da raça Holandesa, foram utilizadas relações entre os seguintes fenótipos: dificuldade de parto, duração da gestação e a proporção de morte perionatal. Nós comparamos o modelo misto de múltiplas características com os modelos de equações estruturais polinomiais, com diferentes graus polinomiais, a fim de verificar os benefícios do MEE polinomial de segundo grau ou superior. Para algumas situações a suposição inapropriada de linearidade resulta em previsões pobres das variâncias e covariâncias genéticas diretas, indiretas e totais, seja por superestimar, subestimar, ou mesmo atribuir sinais opostos as covariâncias. Portanto, verificamos que a inclusão de um grau de polinômio aumenta o poder de expressão do MEE.
APA, Harvard, Vancouver, ISO, and other styles
4

HUANG, BIN. "STATISTICAL ASSESSMENT OF THE CONTRIBUTION OF A MEDIATOR TO AN EXPOSURE OUTCOME PROCESS." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1005678075.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Aten, Jason Erik. "Causal not confounded gene networks inferring acyclic and non-acyclic gene bayesian networks in mRNA expression studies using recursive v-structures, genetic variation, and orthogonal causal anchor structural equation models /." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1563274791&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Fagua, José Camilo. "Geospatial Modeling of Land Cover Change in the Chocó-Darien Global Ecoregion of South America: Assessing Proximate Causes and Underlying Drivers of Deforestation and Reforestation." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7362.

Full text
Abstract:
The Chocó-Darien Global Ecoregion (CGE) in South America is one of 25 global biodiversity hotspots prioritized for conservation. I performed the first land-use and land-cover (LULC) change analysis for the entire CGE in this dissertation. There were three main objectives: 1) Select the best available imagery to build annual land-use and land-cover maps from 2001 to 2015 across the CGE. 2) Model LULC across the CGE to assess forest change trends from 2002 to 2015 and identify the effect of proximate causes of deforestation and reforestation. 3) Estimate the effects of underlying drivers on deforestation and reforestation across the CGE between 2002 and 2015. I developed annual LULC maps across the CGE from 2002 to 2015 using MODIS (Moderate Resolution Imaging Spectro radiometer) vegetation index products and random forest classification. The LULC maps resulted in high accuracies (Kappa = 0.87; SD = 0.008). We detected a gradual replacement of forested areas with agriculture and secondary vegetation (agriculture reverting to early regeneration of natural vegetation) across the CGE. Forest loss was higher between 2010-2015 when compared to 2002-2010. LULC change trends, proximate causes, and reforestation transitions varied according to administrative authority (countries: PanamanianCGE, Colombian CGE, and Ecuadorian CGE). Population growth and road density were underlying drivers of deforestation. Armed conflicts, Gross Domestic Product, and average annual rain were proximate causes and underlying drivers related reforestation.
APA, Harvard, Vancouver, ISO, and other styles
7

Jahanshahi, Kaveh. "Quantification of the influences of built-form upon travel of employed adults : new models based on the UK National Travel Survey." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/267841.

Full text
Abstract:
After decades of research, a host of analytical difficulties is still hindering our understanding of the influences of the built form on travel. The main challenges are (a) assembling good quality data that reflects the majority of the known influences and that supports continuous monitoring, and (b) making sense methodologically of the many variables which strongly intercorrelate. This study uses the UK national travel survey (NTS) data that is among the most comprehensive of its form in the world. The fact that it has rarely been used so far for this purpose may be attributable to the methodological difficulties. This dissertation aims to develop a new analytical framework based on extended structural equation models (SEMs) in order to overcome some of the key methodological difficulties in quantifying the influences of the built form on travel, and in addition to provide a means to continuously monitor any changes in the effects over time. The analyses are focused on employed adults, because they are not only the biggest UK population segment with the highest per capita travel demand, but also the segment that are capable of adapting more rapidly to changing land use, built form and transport supply conditions. The research is pursued through three new models. Model 1 is a path diagram coupled with factor analyses, which estimates continuous, categorical and binary dependent variables. The model estimates the influences on travel distance, time and trip frequency by trip purpose while accounting for self-selection, spatial sorting, endogeneity of car ownership, and interactions among trip purposes. The results highlight stark differences among commuters, particularly the mobility disadvantages of women, part time and non-car owning workers even when they live in the most accessible urban areas. Model 2 incorporates latent categorisation analyses in order to identify a tangible typology of the built form and the associated variations in impacts on travel. Identifying NTS variables as descriptors for tangible built form categories provides an improved basis for investigating land use and transport planning interventions. The model reveals three distinct built form categories in the UK with striking variations in the patterns of influences. Model 3 further investigates the variations across the built form categories. The resulting random intercept SEM provides a more precise quantification of the influences of self-selection and spatial sorting across the built form categories for each socioeconomic group. Four research areas are highlighted for further studies: First, new preference, attitude and behavioural parameters may be introduced through incorporating non-NTS behavioural surveys; Second, the new SEMs provide a basis for incorporating choice modelling where the utility function is defined with direct, indirect and latent variables; Third, conceptual and methodological developments – such as non-parametric latent class analysis, allow expanding the current model to monitor changes in travel behaviour as and when new NTS or non NTS data become available. Fourth, the robustness of the inferences regarding causal or directional influences may require further quantification through designing new panel data sets, building on the findings above.
APA, Harvard, Vancouver, ISO, and other styles
8

Shami, Roland G. (Roland George) 1960. "Bayesian analysis of a structural model with regime switching." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/9277.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wegmann, Bertil. "Bayesian Inference in Structural Second-Price Auctions." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-57278.

Full text
Abstract:
The aim of this thesis is to develop efficient and practically useful Bayesian methods for statistical inference in structural second-price auctions. The models are applied to a carefully collected coin auction dataset with bids and auction-specific characteristics from one thousand Internet auctions on eBay. Bidders are assumed to be risk-neutral and symmetric, and compete for a single object using the same game-theoretic strategy. A key contribution in the thesis is the derivation of very accurate approximations of the otherwise intractable equilibrium bid functions under different model assumptions. These easily computed and numerically stable approximations are shown to be crucial for statistical inference, where the inverse bid functions typically needs to be evaluated several million times. In the first paper, the approximate bid is a linear function of a bidder's signal and a Gaussian common value model is estimated. We find that the publicly available book value and the condition of the auctioned object are important determinants of bidders' valuations, while eBay's detailed seller information is essentially ignored by the bidders. In the second paper, the Gaussian model in the first paper is contrasted to a Gamma model that allows intrinsically non-negative common values. The Gaussian model performs slightly better than the Gamma model on the eBay data, which we attribute to an almost normal or at least symmetrical distribution of valuations. The third paper compares the model in the first paper to a directly comparable model for private values. We find many interesting empirical regularities between the models, but no strong and consistent evidence in favor of one model over the other. In the last paper, we consider auctions with both private-value and common-value bidders. The equilibrium bid function is given as the solution to an ordinary differential equation, from which we derive an approximate inverse bid as an explicit function of a given bid. The paper proposes an elaborate model where the probability of being a common value bidder is a function of covariates at the auction level. The model is estimated by a Metropolis-within-Gibbs algorithm and the results point strongly to an active influx of both private-value and common-value bidders.

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Epub ahead of print. Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.

APA, Harvard, Vancouver, ISO, and other styles
10

BRASIL, GUTEMBERG HESPANHA. "BAYESIAN DYNAMIC MODELLING THE CICLICAL COMPONENT IN STRUCTURAL MODEL FORMULATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1989. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8611@1.

Full text
Abstract:
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Modelos estruturais para séries temporais vêm sendo bastante utilizados ultimamente e, adotam, basicamente, a mesma idéia da decomposição clássica de uma série temporal em seus componentes não-observáveis: tendência, sazonalidade, cíclica e irregular; para a componente cíclica, em particular, que é modelada por uma senóide a amortecida, existem apenas soluções no contexto da Estatística Clássica Harvey (1985). Neste trabalho discutimos extensivamente a solução Bayesiana para o modelo, tornando completamente estocástico a componente ciclo e obtendo um algoritmo para a estimação seqüencial dos parâmetros. A natureza não linear do problema é tratada pelos Modelos Dinâmicos Bayesianos; West e Harrison (1986).
The structural models for time series, so much in use today make use of the well know idea of decomposing a time series into its unobserved components of trend, seasonal, cycle and noise. The cyclical component in particular, which uses a damped sine wave to describe its moviment, has a clear solution available already in computer packages on the Classica framework of Harvey (1985). In this thesis we present a Bayesian solution to the cyclical component modelled by the same damped sine wave. The frequency and the damping factor, regarded as hyperparameters on the Classical solution are now incorporated to the system state vector and estimated by a sequential procedure. Finally, the non-linear nature of model is elegantly dealt with by the Bayesian Dynamic Models of West and Harrison (1986).
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Bayesian structural equation model"

1

Lee, Sik-Yum. Basic and advanced structural equation models for medical and behavioural sciences. Hoboken: Wiley, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Structural equation modeling: A Bayesian approach. Chichester, England: Wiley, 2007.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ghoshal, Sumantra. A structural equation model of scanning behavior of managers. Cambridge, Mass: Massachusetts Institute of Technology, Alfred P. Sloan School of Management, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Hui-xin, Ke. Software system for the analysis of linear structural equation model. Fukuoka, Japan: Kyushu University, Research Institute of Fundamental Inforamtion Science, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Jonsson, Fan Yang. Non-linear structural equation models: Simulation studies of the Kenny-Judd model. Uppsala, Sweden: Uppsala University, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Mishler, William. What are the political consequences of trust?: A Russian structural equation model. Glasgow: University of Strathclyde, Centre for the Study of Public Policy, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Amit, Gupta. Effect of service climate on service quality: Test of a model using structural equation modeling. Bangalore: Indian Institute of Management, 2002.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Lee, Sik-Yum. Structural Equation Modeling: A Bayesian Approach. Wiley & Sons, Incorporated, John, 2007.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Bollen, Kenneth A., Sophia Rabe‐Hesketh, and Anders Skrondal. Structural Equation Models. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0018.

Full text
Abstract:
This article explains the use of factor analysis types of models to develop measures of latent concepts which were then combined with causal models of the underlying latent concepts. In particular, it offers an overview of the classic structural equation models (SEMs) when the latent and observed variables are continuous. Then it looks at more recent developments that include categorical, count, and other noncontinuous variables as well as multilevel structural equation models. The model specification, assumptions, and notation are covered. This is followed by addressing implied moments, identification, estimation, model fit, and respecification. The penetration of SEMs has been high in disciplines such as sociology, psychology, educational testing, and marketing, but lower in economics and political science despite the large potential number of applications. Today, SEMs have begun to enter the statistical literature and to re-enter biostatistics, though often under the name ‘latent variable models’ or ‘graphical models’.
APA, Harvard, Vancouver, ISO, and other styles
10

Miksza, Peter, and Kenneth Elpus. Structural Equation Modeling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0014.

Full text
Abstract:
This chapter presents structural equation modeling as a tool for conducting research regarding how collections of variables may be related to each other as well as to a particular outcome or even multiple outcomes. Structural equation modeling refers to a collection of analytical techniques that can be used to model complex patterns of predictive relationships among a collection of both measured and latent variables. As a statistical tool, structural equation modeling combines the features of regression and factor analysis. The chapter offers conceptual illustrations and practical steps for carrying out structural equation modeling by describing mediation and moderation analyses in the context of music education research.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Bayesian structural equation model"

1

Lee, Sik-Yum, and Xin-Yuan Song. "Bayesian Model Comparison of Structural Equation Models." In Random Effect and Latent Variable Model Selection, 121–50. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-76721-5_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Stern, Hal S., and Yoonsook Jeon. "Applying Structural Equation Models with Incomplete Data." In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 331–42. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470090456.ch30.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Dijk, Herman K. van. "Chapter 25. On Bayesian Structural Inference in a Simultaneous Equation Model." In Econometrics and the Philosophy of Economics, 642–82. Princeton: Princeton University Press, 2003. http://dx.doi.org/10.1515/9781400873234-028.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Shigemasu, Kazuo, Takahiro Hoshino, and Takuya Ohmori. "Bayesian Analysis of Structural Equation Modeling." In Measurement and Multivariate Analysis, 207–16. Tokyo: Springer Japan, 2002. http://dx.doi.org/10.1007/978-4-431-65955-6_22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Demeyer, Séverine, Nicolas Fischer, and Gilbert Saporta. "Contributions to Bayesian Structural Equation Modeling." In Proceedings of COMPSTAT'2010, 469–76. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_46.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Bretthorst, G. Larry. "The General Model Equation Plus Noise." In Bayesian Spectrum Analysis and Parameter Estimation, 31–41. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4684-9399-3_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Caicedo, Juan M., Boris A. Zárate, Victor Giurgiutiu, Lingyu Yu, and Paul Ziehl. "Bayesian Finite Element Model Updating for Crack Growth." In Structural Dynamics, Volume 3, 861–66. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9834-7_76.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Rogers, Timothy J., Keith Worden, and Elizabeth J. Cross. "Bayesian Solutions to State-Space Structural Identification." In Model Validation and Uncertainty Quantification, Volume 3, 247–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0_27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wedel, Michel, and Wagner A. Kamakura. "Model-Based Segmentation Using Structural Equation Models." In International Series in Quantitative Marketing, 217–29. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4651-1_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Zárate, Boris A., Juan M. Caicedo, Glen Wieger, and Johannio Marulanda. "Bayesian Finite Element Model Updating Using Static and Dynamic Data." In Structural Dynamics, Volume 3, 395–402. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9834-7_38.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Bayesian structural equation model"

1

Thanoon, Thanoon Y., and Robiah Adnan. "Comparison between Bayesian structural equation models with ordered categorical data." In ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). Author(s), 2016. http://dx.doi.org/10.1063/1.4954631.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

"Combining Structure Equation Model with Bayesian Networks for predicting with high accuracy of recommending surgery for better survival in Benign prostatic hyperplasia patients." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.i4.yoo3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

May, Allan, David McMillan, and Sebastian Thöns. "Integrating Structural Health and Condition Monitoring: A Cost Benefit Analysis for Offshore Wind Energy." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41126.

Full text
Abstract:
There is a large financial incentive to minimise operations and maintenance (O&M) costs for offshore wind power by optimising the maintenance plan. The integration of condition monitoring (CM) and structural health monitoring (SHM) may help realise this. There is limited work on the integration of both CM and SHM for offshore wind power or the use of imperfectly operating monitoring equipment. In order to investigate this, a dynamic Bayesian network and limit state equations are coupled with Monte Carlo simulations to deteriorate components in a wind farm. The CM system has a ‘deterioration window’ allowing for the possible detection of faults up to 6 months in advance. The SHM system model uses a reduction in the probability of failure factor to account for lower modelling uncertainties. A case study is produced that shows a reduction in operating costs and also a reduction in risk. The lifetime levelised costs are reduced by approximately 6%.
APA, Harvard, Vancouver, ISO, and other styles
4

de Waal, Alta, and Keunyoung Yoo. "Latent Variable Bayesian Networks Constructed Using Structural Equation Modelling." In 2018 International Conference on Information Fusion (FUSION). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455240.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Yanuar, Ferra, and Aidinil Zetra. "Simulation Study to Describe Bayesian Analysis of Nonlinear Structural Equation Modeling." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290337.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Tasaka, Shuji. "Bayesian structural equation modeling of multidimensional QoE in haptic-audiovisual interactive communications." In ICC 2016 - 2016 IEEE International Conference on Communications. IEEE, 2016. http://dx.doi.org/10.1109/icc.2016.7511202.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ishita, Emi, Yosuke Miyata, Shuichi Ueda, and Keiko Kurata. "A Structural Equation Model of Information Retrieval Skills." In CHIIR '17: Conference on Human Information Interaction and Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3020165.3022142.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

GARDNER, PAUL, ROBERT J. BARTHORPE, and CHARLES LORD. "Bayesian Calibration and Bias Correction for Forward Model-driven SHM." In Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/14088.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Muangpan, T., M. Chaowarat, and J. Neamvonk. "Performance Model of Sustainable Supply Chain Management: The Structural Equation Model." In Annual International Conference on Sustainable Energy and Environmental Sciences. Global Science & Technology Forum (GSTF), 2015. http://dx.doi.org/10.5176/2251-189x_sees15.21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hu, Shu-Jen, Sin-Ying Jou, and Yu-Hua Liu. "Structural Equation Model for Brand Image Measurement of Jeans." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.25.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Bayesian structural equation model"

1

Beron, Kurt, Helen Tauchen, and Ann Dryden Witte. A Structural Equation Model for Tax Compliance and Auditing. Cambridge, MA: National Bureau of Economic Research, April 1988. http://dx.doi.org/10.3386/w2556.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Wilcove, Gerry L., Donna G. Wolosin, and Michael J. Schwerin. Development of a New Quality of Life (QOL) Model Using Structural Equation Modeling. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada405971.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Groeneveld, Andrew B., Stephanie G. Wood, and Edgardo Ruiz. Estimating Bridge Reliability by Using Bayesian Networks. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39601.

Full text
Abstract:
As part of an inspection, bridge inspectors assign condition ratings to the main components of a bridge’s structural system and identify any defects that they observe. Condition ratings are necessarily somewhat subjective, as they are influenced by the experience of the inspectors. In the current work, procedures were developed for making inferences on the reliability of reinforced concrete girders with defects at both the cross section and the girder level. The Bayesian network (BN) tools constructed in this work use simple structural m echanics to model the capacity of girders. By using expert elicitation, defects observed during inspection are correlated with underlying deterioration mechanisms. By linking these deterioration mechanisms with reductions in mechanical properties, inferences on the reliability of a bridge can be made based on visual observation of defects. With more development, this BN tool can be used to compare conditions of bridges relative to one another and aid in the prioritization of repairs. However, an extensive survey of bridges affected by deterioration mechanisms is needed to confidently establish valid relationships between deterioration severity and mechanical properties.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Bayesian structural equation model' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography