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Basic and advanced Bayesian structural equation modeling : with applications in the medical and behavioral sciences /
"This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables. Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various...
| Call Number: | Libro Electrónico |
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| Main Author: | |
| Other Authors: | |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
Chichester, West Sussex :
John Wiley & Sons,
2012.
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| Subjects: | |
| Online Access: | Texto completo |
MARC
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| 082 | 0 | 4 | |a 519.5/3 |a 519.53 |
| 084 | |a MAT029000 |2 bisacsh | ||
| 049 | |a UAMI | ||
| 100 | 1 | |a Song, Xin-Yuan. | |
| 245 | 1 | 0 | |a Basic and advanced Bayesian structural equation modeling : |b with applications in the medical and behavioral sciences / |c Xin-Yuan Song and Sik-Yum Lee. |
| 260 | |a Chichester, West Sussex : |b John Wiley & Sons, |c 2012. | ||
| 300 | |a 1 online resource (xvii, 367 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |2 rdaft | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |6 880-01 |a Introduction -- Basic concepts and applications of structural equation models -- Bayesian methods for estimating structural equation models -- Bayesian model comparison and model checking -- Practical structural equation models -- Structural equation models with hierarchical and multisample data -- Mixture structural equation models -- Structural equation modeling for latent curve models -- Longitudinal structural equation models -- Semiparametric structural equation models with continuous variables -- Structural equation models with mixed continuous and unordered categorical variables -- Structural equation models with nonparametric structural equations -- Transformation structural equation models -- Conclusion. | |
| 520 | |a "This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables. Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced, whilst SEM with a nonparametric structural equation to assess unspecified functional relationships among latent variables are also explored. Statistical methodologies are developed using the Bayesian approach giving reliable results for small samples and allowing the use of prior information leading to better statistical results. Estimates of the parameters and model comparison statistics are obtained via powerful Markov Chain Monte Carlo methods in statistical computing."--Publisher's website. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 0 | |a Structural equation modeling. | |
| 650 | 0 | |a Multivariate analysis. | |
| 650 | 2 | 2 | |a Bayes Theorem |
| 650 | 2 | 2 | |a Models, Statistical |
| 650 | 2 | 2 | |a Multivariate Analysis |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 6 | |a Modèles d'équations structurales. | |
| 650 | 6 | |a Théorème de Bayes. | |
| 650 | 6 | |a Analyse multivariée. | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Multivariate analysis |2 fast | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Structural equation modeling |2 fast | |
| 650 | 7 | |a Strukturgleichungsmodell |2 gnd | |
| 700 | 1 | |a Lee, Sik-Yum. | |
| 758 | |i has work: |a Basic and advanced Bayesian structural equation modeling (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGby3rGK7HdgfGVhbmmmkC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Song, Xin-Yuan. |t Basic and advanced Bayesian structural equation modeling. |d Chichester, West Sussex : John Wiley, 2012 |z 9780470669525 |w (DLC) 2012012199 |w (OCoLC)853461614 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=954614 |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g 1. |t Introduction -- |g 1.1. |t Observed and latent variables -- |g 1.2. |t Structural equation model -- |g 1.3. |t Objectives of the book -- |g 1.4. |t Bayesian approach -- |g 1.5. |t Real data sets and notation -- |g Appendix 1.1 |t Information on real data sets -- |t References -- |g 2. |t Basic concepts and applications of structural equation models -- |g 2.1. |t Introduction -- |g 2.2. |t Linear SEMs -- |g 2.2.1. |t Measurement equation -- |g 2.2.2. |t Structural equation and one extension -- |g 2.2.3. |t Assumptions of linear SEMs -- |g 2.2.4. |t Model identification -- |g 2.2.5. |t Path diagram -- |g 2.3. |t SEMs with fixed covariates -- |g 2.3.1. |t model -- |g 2.3.2. |t artificial example -- |g 2.4. |t Nonlinear SEMs -- |g 2.4.1. |t Basic nonlinear SEMs -- |g 2.4.2. |t Nonlinear SEMs with fixed covariates -- |g 2.4.3. |t Remarks -- |g 2.5. |t Discussion and conclusions -- |t References -- |g 3. |t Bayesian methods for estimating structural equation models -- |g 3.1. |t Introduction -- |g 3.2. |t Basic concepts of the Bayesian estimation and prior distributions -- |g 3.2.1. |t Prior distributions -- |g 3.2.2. |t Conjugate prior distributions in Bayesian analyses of SEMs -- |g 3.3. |t Posterior analysis using Markov chain Monte Carlo methods -- |g 3.4. |t Application of Markov chain Monte Carlo methods -- |g 3.5. |t Bayesian estimation via WinBUGS -- |g Appendix 3.1 |t gamma, inverted gamma, Wishart, and inverted Wishart distributions and their characteristics -- |g Appendix 3.2 |t Metropolis-Hastings algorithm -- |g Appendix 3.3 |t Conditional distributions [Ω |g Appendix 3.4 |t Conditional distributions [Ω |g Appendix 3.5 |t WinBUGS code -- |g Appendix 3.6 |t R2WinBUGS code -- |t References -- |g 4. |t Bayesian model comparison and model checking -- |g 4.1. |t Introduction -- |g 4.2. |t Bayes factor -- |g 4.2.1. |t Path sampling -- |g 4.2.2. |t simulation study -- |g 4.3. |t Other model comparison statistics -- |g 4.3.1. |t Bayesian information criterion and Akaike information criterion -- |g 4.3.2. |t Deviance information criterion -- |g 4.3.3. |t Lv-measure -- |g 4.4. |t Illustration -- |g 4.5. |t Goodness of lit and model checking methods -- |g 4.5.1. |t Posterior predictive p-value -- |g 4.5.2. |t Residual analysis -- |g Appendix 4.1 |t WinBUGS code -- |g Appendix 4.2 |t R code in Bayes factor example -- |g Appendix 4.3 |t Posterior predictive p-value for model assessment -- |t References -- |g 5. |t Practical structural equation models -- |g 5.1. |t Introduction -- |g 5.2. |t SEMs with continuous and ordered categorical variables -- |g 5.2.1. |t Introduction -- |g 5.2.2. |t basic model -- |g 5.2.3. |t Bayesian analysis -- |g 5.2.4. |t Application: Bayesian analysis of quality of life data -- |g 5.2.5. |t SEMs with dichotomous variables -- |g 5.3. |t SEMs with variables from exponential family distributions -- |g 5.3.1. |t Introduction -- |g 5.3.2. |t SEM framework with exponential family distributions -- |g 5.3.3. |t Bayesian inference -- |g 5.3.4. |t Simulation study -- |g 5.4. |t SEMs with missing data -- |g 5.4.1. |t Introduction -- |g 5.4.2. |t SEMs with missing data that are MAR -- |g 5.4.3. |t illustrative example -- |g 5.4.4. |t Nonlinear SEMs with nonignorable missing data -- |g 5.4.5. |t illustrative real example -- |g Appendix 5.1 |t Conditional distributions and implementation of the MH algorithm for SEMs with continuous and ordered categorical variables -- |g Appendix 5.2 |t Conditional distributions and implementation of MH algorithm for SEMs with EFDs -- |g Appendix 5.3 |t WinBUGS code related to section 5.3.4 -- |g Appendix 5.4 |t R2WinBUGS code related to section 5.3.4 -- |g Appendix 5.5 |t Conditional distributions for SEMs with nonignorable missing data -- |t References -- |g 6. |t Structural equation models with hierarchical and multisample data -- |g 6.1. |t Introduction -- |g 6.2. |t Two-level structural equation models -- |g 6.2.1. |t Two-level nonlinear SEM with mixed type variables -- |g 6.2.2. |t Bayesian inference -- |g 6.2.3. |t Application: Filipina CSWs study -- |g 6.3. |t Structural equation models with multisample data -- |g 6.3.1. |t Bayesian analysis of a nonlinear SEM in different groups -- |g 6.3.2. |t Analysis of multisample quality of life data via WinBUGS -- |g Appendix 6.1 |t Conditional distributions: Two-level nonlinear SEM -- |g Appendix 6.2 |t MH algorithm: Two-level nonlinear SEM -- |g Appendix 6.3 |t PP p-value for two-level nonlinear SEM with mixed continuous and ordered categorical variables -- |g Appendix 6.4 |t WinBUGS code -- |g Appendix 6.5 |t Conditional distributions: Multisample SEMs -- |t References -- |g 7. |t Mixture structural equation models -- |g 7.1. |t Introduction -- |g 7.2. |t Finite mixture SEMs -- |g 7.2.1. |t model -- |g 7.2.2. |t Bayesian estimation -- |g 7.2.3. |t Analysis of an artificial example -- |g 7.2.4. |t Example from the world values survey -- |g 7.2.5. |t Bayesian model comparison of mixture SEMs -- |g 7.2.6. |t illustrative example -- |g 7.3. |t Modified mixture SEM -- |g 7.3.1. |t Model description -- |g 7.3.2. |t Bayesian estimation -- |g 7.3.3. |t Bayesian model selection using a modified DIC -- |g 7.3.4. |t illustrative example -- |g Appendix 7.1 |t permutation sampler -- |g Appendix 7.2 |t Searching for identifiability constraints -- |g Appendix 7.3 |t Conditional distributions: Modified mixture SEMs -- |t References -- |g 8. |t Structural equation modeling for latent curve models -- |g 8.1. |t Introduction -- |g 8.2. |t Background to the real studies -- |g 8.2.1. |t longitudinal study of quality of life of stroke survivors -- |g 8.2.2. |t longitudinal study of cocaine use -- |g 8.3. |t Latent curve models -- |g 8.3.1. |t Basic latent curve models -- |g 8.3.2. |t Latent curve models with explanatory latent variables -- |g 8.3.3. |t Latent curve models with longitudinal latent variables -- |g 8.4. |t Bayesian analysis -- |g 8.5. |t Applications to two longitudinal studies -- |g 8.5.1. |t Longitudinal study of cocaine use -- |g 8.5.2. |t Health-related quality of life for stroke survivors -- |g 8.6. |t Other latent curve models -- |g 8.6.1. |t Nonlinear latent curve models -- |g 8.6.2. |t Multilevel latent curve models -- |g 8.6.3. |t Mixture latent curve models -- |g Appendix 8.1 |t Conditional distributions -- |g Appendix 8.2 |t WinBUGS code for the analysis of cocaine use data -- |t References -- |g 9. |t Longitudinal structural equation models -- |g 9.1. |t Introduction -- |g 9.2. |t two-level SEM for analyzing multivariate longitudinal data -- |g 9.3. |t Bayesian analysis of the two-level longitudinal SEM -- |g 9.3.1. |t Bayesian estimation -- |g 9.3.2. |t Model comparison via the Lv-measure -- |g 9.4. |t Simulation study -- |g 9.5. |t Application: Longitudinal study of cocaine use -- |g 9.6. |t Discussion -- |g Appendix 9.1 |t Full conditional distributions for implementing the Gibbs sampler -- |g Appendix 9.2 |t Approximation of the Lv-measure in equation (9.9) via MCMC samples -- |t References -- |g 10. |t Semiparametric structural equation models with continuous variables -- |g 10.1. |t Introduction -- |g 10.2. |t Bayesian semiparametric hierarchical modeling of SEMs with covariates -- |g 10.3. |t Bayesian estimation and model comparison -- |g 10.4. |t Application: Kidney disease study -- |g 10.5. |t Simulation studies -- |g 10.5.1. |t Simulation study of estimation -- |g 10.5.2. |t Simulation study of model comparison -- |g 10.5.3. |t Obtaining the Lv-measure via WinBUGS and R2WinBUGS -- |g 10.6. |t Discussion -- |g Appendix 10.1 |t Conditional distributions for parametric components -- |g Appendix 10.2 |t Conditional distributions for nonparametric components -- |t References -- |g 11. |t Structural equation models with mixed continuous and unordered categorical variables -- |g 11.1. |t Introduction -- |g 11.2. |t Parametric SEMs with continuous and unordered categorical variables -- |g 11.2.1. |t model -- |g 11.2.2. |t Application to diabetic kidney disease -- |g 11.2.3. |t Bayesian estimation and model comparison -- |g 11.2.4. |t Application to the diabetic kidney disease data -- |g 11.3. |t Bayesian semiparametric SEM with continuous and unordered categorical variables -- |g 11.3.1. |t Formulation of the semiparametric SEM -- |g 11.3.2. |t Semiparametric hierarchical modeling via the Dirichlet process -- |g 11.3.3. |t Estimation and model comparison -- |g 11.3.4. |t Simulation study -- |g 11.3.5. |t Real example: Diabetic nephropathy study -- |g Appendix 11.1 |t Full conditional distributions -- |g Appendix 11.2 |t Path sampling -- |g Appendix 11.3 |t modified truncated DP related to equation (11.19) -- |g Appendix 11.4 |t Conditional distributions and the MH algorithm for the Bayesian semiparametric model -- |t References -- |g 12. |t Structural equation models with nonparametric structural equations -- |g 12.1. |t Introduction -- |g 12.2. |t Nonparametric SEMs with Bayesian P-splines -- |g 12.2.1. |t Model description -- |g 12.2.2. |t General formulation of the Bayesian P-splines -- |g 12.2.3. |t Modeling nonparametric functions of latent |
| 880 | 0 | 0 | |t variables -- |g 12.2.4. |t Prior distributions -- |g 12.2.5. |t Posterior inference via Markov chain Monte Carlo sampling -- |g 12.2.6. |t Simulation study -- |g 12.2.7. |t study on osteoporosis prevention and control -- |g 12.3. |t Generalized nonparametric structural equation models -- |g 12.3.1. |t Model description -- |g 12.3.2. |t Bayesian P-splines -- |g 12.3.3. |t Prior distributions -- |g 12.3.4. |t Bayesian estimation and model comparison -- |g 12.3.5. |t National longitudinal surveys of youth study -- |g 12.4. |t Discussion -- |g Appendix 12.1 |t Conditional distributions and the MH algorithm: Nonparametric SEMs -- |g Appendix 12.2 |t Conditional distributions in generalized nonparametric SEMs -- |t References -- |g 13. |t Transformation structural equation models -- |g 13.1. |t Introduction -- |g 13.2. |t Model description -- |g 13.3. |t Modeling nonparametric transformations -- |g 13.4. |t Identifiability constraints and prior distributions -- |g 13.5. |t Posterior inference with MCMC algorithms -- |g 13.5.1. |t Conditional distributions -- |g 13.5.2. |t random-ray algorithm -- |g 13.5.3. |t Modifications of the random-ray algorithm -- |g 13.6. |t Simulation study -- |g 13.7. |t study on the intervention treatment of polydrug use. |
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