Structural Equation Modeling: A Bayesian Approach (Wiley Series in Probability and Statistics)

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9780470024232: Structural Equation Modeling: A Bayesian Approach (Wiley Series in Probability and Statistics)
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"This book is a welcome addition to any library and should be a valuable resource for research and teaching." (Technometrics, August 2008)

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Winner of the 2008 Ziegel Prize for outstanding new book of the year Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject's recent advances. * Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results. * Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison. * Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations. * Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology. * Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets. Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.

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Lee, Sik-yum
Editorial: John Wiley & Sons Inc, Somerset, New Jersey, U.S.A. (2007)
ISBN 10: 0470024232 ISBN 13: 9780470024232
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Descripción John Wiley & Sons Inc, Somerset, New Jersey, U.S.A., 2007. Hardcover. Estado de conservación: New. No Jacket. A new copy of the hard cover edition, lacking a dust-jacket, but still in publisher's shrinkwrap. A brilliant copy. Nº de ref. de la librería 016521

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Lee, Sik-yum
Editorial: Somerset, New Jersey, U.S.A.: John Wiley & Sons Inc (2007)
ISBN 10: 0470024232 ISBN 13: 9780470024232
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Descripción Somerset, New Jersey, U.S.A.: John Wiley & Sons Inc, 2007. Hardcover. Estado de conservación: New. Ship out 1-2 business day,Brand new,US edition, Free tracking number usually 2-4 biz days delivery to worldwide Same shipping fee with US, Canada,Europe country, Australia, item will ship out from either USA or Asia,lt. Nº de ref. de la librería ABE-6381761401

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Descripción 2007. Hardback. Estado de conservación: NEW. 9780470024232 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Nº de ref. de la librería HTANDREE0765588

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Descripción John Wiley and Sons Ltd, United States, 2007. Hardback. Estado de conservación: New. 1. Auflage. 234 x 160 mm. Language: English . Brand New Book ***** Print on Demand *****.Winner of the 2008 Ziegel Prize for outstanding new book of the year Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject s recent advances. * Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results. * Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison. * Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations. * Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology. * Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets. Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science. Nº de ref. de la librería APC9780470024232

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Sik-Yum Lee
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ISBN 10: 0470024232 ISBN 13: 9780470024232
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Descripción John Wiley and Sons Ltd, United States, 2007. Hardback. Estado de conservación: New. 1. Auflage. 234 x 160 mm. Language: English . Brand New Book ***** Print on Demand *****. Winner of the 2008 Ziegel Prize for outstanding new book of the year Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject s recent advances. * Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results. * Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison. * Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations. * Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology. * Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets. Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science. Nº de ref. de la librería APC9780470024232

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Lee, Sik-Yum
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Descripción Wiley, 2017. Hardcover. Estado de conservación: New. This item is printed on demand. Nº de ref. de la librería 0470024232

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Descripción Estado de conservación: New. This item is Print on Demand - Depending on your location, this item may ship from the US or UK. Nº de ref. de la librería POD_9780470024232

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Descripción Wiley. Hardcover. Estado de conservación: New. Hardcover. 458 pages. Dimensions: 9.2in. x 6.3in. x 1.2in.Winner of the 2008 Ziegel Prize for outstanding new book of the yearStructural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subjects recent advances. Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results. Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison. Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations. Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology. Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets. Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Hardcover. Nº de ref. de la librería 9780470024232

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