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  • 1
    Online Resource
    Online Resource
    Handbook of latent variable and related models (2007)
    Amsterdam ; Boston : Elsevier/North-Holland
    Details
    UID:
    b3kat_BV036962177
    Format: 1 Online-Ressource (xxii, 435 p.) , ill , 25 cm
    Edition: 1st ed
    Edition: Online-Ausgabe Elsevier e-book collection on ScienceDirect Sonstige Standardnummer des Gesamttitels: 041169-3
    ISBN: 0444520449 , 9780444520449
    Series Statement: Handbook of computing and statistics with applications v. 1
    Note: Includes bibliographical references and indexes
    Additional Edition: Reproduktion von Handbook of latent variable and related models 2007
    Language: English
    Subjects: Mathematics
    RVK:
    SK 830
    Keywords: Multivariate Analyse ; Statistik ; Datenverarbeitung
    URL: Volltext
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    Online Resource
    Online Access
    HWR Berlin
    EUV Frankfurt
    TH Brandenburg
    HTW Berlin
    TH Wildau
    BTU Cottbus
    BHT
  • 2
    Online Resource
    Online Resource
    Handbook of latent variable and related models / (2007)
    Amsterdam ; : Elsevier,
    Details
    UID:
    almahu_9947367657102882
    Format: 1 online resource (458 p.)
    Edition: 1st ed.
    ISBN: 1-280-96270-4 , 9786610962709 , 0-08-047126-9
    Series Statement: Handbook of computing and statistics with applications ; v. 1
    Content: This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.
    Note: Description based upon print version of record. , Front cover; Handbook of Latent Variable and Related Models; Copyright page; Handbook Series on Computing and Statistics with Applications; Preface; About the Authors; Contributors; Table of contents; Chapter 1.Covariance Structure Models for Maximal Reliability of Unit-Weighted Composites; 1. Proposed identification condition for factor models; 2. Reliability based on proposed parameterization; 3. Properties of the coefficient; 4. Illustration with exploratory factor analysis; 5. Reliability with general latent variable models; 6. Dimension-free and greatest lower bound reliability , 7. Reliability of weighted composites8. Selection of weights for maximal reliability; 9. Conclusions; Acknowledgements; References; Chapter 2. Advances in Analysis of Mean and Covariance Structure when Data are Incomplete; 1. Introduction; 2. Missing data mechanism; 3. Methods for handling missing data; 4. Simulation studies; 5. Sensitivity analysis for missing data mechanism; 6. SEM software for incomplete data; References; Chapter 3. Rotation Algorithms: From Beginning to End; 1. Introduction; 2. Factor analysis; 3. A parameterization for Lambda and Phi; 4. Reference structures , 5. Thurstone's graphical rotation method6. Early analytic oblique rotation methods; 7. Pairwise algorithms; 8. Analytic rotation methods: Orthogonal; 9. Direct analytic methods: Oblique; 10. Discussion; References; Chapter 4. Selection of Manifest Variables; 1. Introduction; 2. Manifest variable selection in factor analysis; 3. SEFA and examples with empirical data; 4. Variable selection with a model fit and reliability analysis; 5. Conclusion and final remarks; Acknowledgements; References; Chapter 5. Bayesian Analysis of Mixtures Structural Equation Models with Missing Data; 1. Introduction , 2. Model description3. Bayesian analysis of the models; 4. Simulation studies; 5. An illustrative example; 6. Analysis via WinBUGS; 7. Discussion; Acknowledgements; Appendix A. The permutation sampler; Appendix B. Searching for identifiability constraints; Appendix C. Manifest variables in the ICPSR example; References; Chapter 6. Local Influence Analysis for Latent Variable Models with Non-Ignorable Missing Responses; 1. Introduction; 2. Local influence of latent variable models with non-ignorable missing data; 3. Normal mixed effects model; 4. Generalized linear mixed model; 5. Conclusion , Appendix AAppendix B; Appendix C; References; Chapter 7. Goodness-of-Fit Measures for Latent Variable Models for Binary Data; 1. Introduction; 2. Latent variable models for binary responses; 3. Goodness-of-fit tests for latent variable models for binary data; 4. Limited information statistics; 5. Test based on the log-odds ratio; 6. Simulations; 7. Conclusion; Acknowledgements; References; Chapter 8 Bayesian Structural Equation Modeling; 1. Introduction; 2. Structural equation models; 3. Bayesian approach; 4. Democratization and industrialization application; 5. Discussion and future research , Appendix A. Prior specifications , English
    Additional Edition: ISBN 0-444-52044-9
    Language: English
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    Online Resource
    HU Berlin
    FU Berlin
    Charité
  • 3
    Online Resource
    Online Resource
    Handbook of latent variable and related models (2007)
    Amsterdam [u.a.] : Elsevier
    Details
    UID:
    gbv_1645856364
    Format: Online Ressource (xxii, 435 p.) , illustrations.
    Edition: 1st ed.
    Edition: Online-Ressource ScienceDirect
    ISBN: 9780444520449 , 0444520449 , 9780080471266 , 0080471269
    Series Statement: Handbook of computing and statistics with applications v. 1
    Content: Preface -- About the Authors -- 1. Covariance Structure Models for Maximal Reliability of Unit-weighted Composites (Peter M. Bentler) -- 2. Advances in Analysis of Mean and Covariance Structure When Data are Incomplete (Mortaza Jamshidian, Matthew Mata) -- 3. Rotation Algorithms: From Beginning to End (Robert I. Jennrich) -- 4. Selection of Manifest Variables (Yutaka Kano) -- 5. Bayesian Analysis of Mixtures Structural Equation Models with Missing Data (Sik-Yum Lee) -- 6. Local Influence Analysis for Latent Variable Models with Nonignorable Missing Responses (Bin Lu, Xin-Yuan Song, Sik-Yum Lee, Fernand Mac-Moune Lai) -- 7. Goodness-of-fit Measures for Latent Variable Models for Binary Data (D. Mavridis, Irini Moustaki, Martin Knott) -- 8. Bayesian Structural Equation Modeling (Jesus Palomo, David B. Dunson, Ken Bollen) -- 9. The Analysis of Structural Equation Model with Ranking Data using Mx (Wai-Yin Poon) -- 10. Multilevel Structural Equation Modeling (Sophia Rable-Hesketh, Anders Skrondal, Xiaohui Zheng) -- 11. Statistical Inference of Moment Structure (Alexander Shapiro) -- 12. Meta-Analysis and Latent Variables Models for Binary Data (Jian-Qing Shi) -- 13. Analysis of Multisample Structural Equation Models with Applications to Quality of Life Data (Xin-Yuan Song) -- 14. The Set of Feasible Solutions for Reliability and Factor Analysis (Jos M.F. ten Berge, Gregor Söan) -- 15. Nonlinear Structural Equation Modeling as a Statistical Method (Melanie M. Wall, Yasuo Amemiya) -- 16. Matrix Methods and Their Applications to Factor Analysis (Haruo Yanai, Yoshio Takane) -- 17. Robust Procedures in Structural Equation Modeling (Ke-Hai Yuan, Peter M. Bentler) -- 18. Stochastic Approximation Algorithms for Estimation of Spatial Mixed Models (Hongtu Zhu, Faming Liang, Minggao Gu, Bradley Peterson)
    Content: This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables. - Covers a wide class of important models - Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data - Includes illustrative examples with real data sets from business, education, medicine, public health and sociology. - Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques
    Note: Includes bibliographical references and indexes. - Description based on print version record
    Additional Edition: ISBN 0444520449
    Additional Edition: Druckausg. Handbook of latent variable and related models Amsterdam : Elsevier, 2008 ISBN 9780444520449
    Language: English
    Subjects: Mathematics
    RVK:
    SK 830
    Keywords: Datenverarbeitung ; Multivariate Analyse ; Statistik ; Electronic books ; Electronic books
    DOI: 10.1016/B978-0-444-52044-9.X5000-9
    URL: https://doi.org/10.1016/B978-0-444-52044-9.X5000-9
    URL: Volltext  (Deutschlandweit zugänglich)
    URL: lizenzpflichtig
    URL: Inhaltstext
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    Open Access Request
    Stabi Berlin
    UB Potsdam
  • 4
    Book
    Book
    Structural equation modeling : a Bayesian approach (2008)
    Chichester [u.a.] :Wiley,
    Details
    UID:
    almafu_BV035962354
    Format: XV, 432 S. : , graph. Darst.
    Edition: Reprint.
    ISBN: 978-0-470-02423-2 , 0-470-02423-2
    Series Statement: Wiley series in probability and statistics
    Language: English
    Subjects: Economics , Mathematics
    RVK:
    QH 234
    RVK:
    SK 840
    Keywords: Bayes-Entscheidungstheorie ; Strukturgleichungsmodell ; Bayes-Verfahren
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  • 5
    Online Resource
    Online Resource
    Structural equation modeling : a Bayesian approach (2007)
    Chichester, England ; : Wiley,
    Details
    UID:
    almafu_9959326946402883
    Format: 1 online resource (xv, 432 pages) : , illustrations
    ISBN: 9780470024737 , 0470024739 , 9780470024249 , 0470024240
    Series Statement: Wiley series in probability and statistics
    Content: 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.
    Note: Structural Equation Modeling; Contents; About the Author; Preface; 1 Introduction; 2 Some Basic Structural Equation Models; 3 Covariance Structure Analysis; 4 Bayesian Estimation of Structural Equation Models; 5 Model Comparison and Model Checking; 6 Structural Equation Models with Continuous and Ordered Categorical Variables; 7 Structural Equation Models with Dichotomous Variables; 8 Nonlinear Structural Equation Models; 9 Two-level Nonlinear Structural Equation Models; 10 Multisample Analysis of Structural Equation Models; 11 Finite Mixtures in Structural Equation Models.
    Additional Edition: Print version: Lee, Sik-Yum. Structural equation modeling. Chichester, England ; Hoboken, NJ : Wiley, ©2007 ISBN 0470024232
    Additional Edition: ISBN 9780470024232
    Language: English
    Keywords: Electronic books. ; Electronic books. ; Electronic books.
    DOI: 10.1002/9780470024737
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470024737
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470024737
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470024737
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    HU Berlin
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  • 6
    Online Resource
    Online Resource
    Basic and advanced Bayesian structural equation modeling : with applications in the medical and behavioural sciences (2012)
    Hoboken, NJ :Wiley,
    Details
    UID:
    almafu_9959328282502883
    Format: 1 online resource (xvii, 367 pages) : , illustrations
    ISBN: 0470669527 , 9780470669525 , 9781280879951 , 1280879955 , 9781118358887 , 1118358880 , 9781118359433 , 1118359437
    Series Statement: Wiley Series in Probability and Statistics
    Content: 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.
    Note: 3.5 Bayesian estimation via WinBUGSAppendix 3.1: The gamma, inverted gamma, Wishart, and invertedWishart distributions and their characteristics; Appendix 3.2: The Metropolis-Hastings algorithm; Appendix 3.3: Conditional distributions [Omega, gamma, theta] and [Omega, gamma, theta]; Appendix 3.4: Conditional distributions [Omega, gamma, theta] and [Omega, gamma, theta] in nonlinear SEMs with covariates; Appendix 3.5: WinBUGS code; Appendix 3.6: R2WinBUGS code; References; 4 Bayesian model comparison and model checking; 4.1 Introduction; 4.2 Bayes factor; 4.2.1 Path sampling; 4.2.2 A simulation study; 4.3 Other model comparison statistics , Basic and Advanced Bayesian Structural Equation Modeling; Contents; About the authors; Preface; 1 Introduction; 1.1 Observed and latent variables; 1.2 Structural equation model; 1.3 Objectives of the book; 1.4 The Bayesian approach; 1.5 Real data sets and notation; Appendix 1.1: Information on real data sets; References; 2 Basic concepts and applications of structural equation models; 2.1 Introduction; 2.2 Linear SEMs; 2.2.1 Measurement equation; 2.2.2 Structural equation and one extension; 2.2.3 Assumptions of linear SEMs; 2.2.4 Model identification; 2.2.5 Path diagram , 2.3 SEMs with fixed covariates2.3.1 The model; 2.3.2 An artificial example; 2.4 Nonlinear SEMs; 2.4.1 Basic nonlinear SEMs; 2.4.2 Nonlinear SEMs with fixed covariates; 2.4.3 Remarks; 2.5 Discussion and conclusions; References; 3 Bayesian methods for estimating structural equation models; 3.1 Introduction; 3.2 Basic concepts of the Bayesian estimation and prior distributions; 3.2.1 Prior distributions; 3.2.2 Conjugate prior distributions in Bayesian analyses of SEMs; 3.3 Posterior analysis using Markov chain Monte Carlo methods; 3.4 Application of Markov chain Monte Carlo methods , 5.2.4 Application: Bayesian analysis of quality of life data5.2.5 SEMs with dichotomous variables; 5.3 SEMs with variables from exponential family distributions; 5.3.1 Introduction; 5.3.2 The SEM framework with exponential family distributions; 5.3.3 Bayesian inference; 5.3.4 Simulation study; 5.4 SEMs with missing data; 5.4.1 Introduction; 5.4.2 SEMs with missing data that are MAR; 5.4.3 An illustrative example; 5.4.4 Nonlinear SEMs with nonignorable missing data; 5.4.5 An illustrative real example
    Additional Edition: Print version: ISBN 9781280879951
    Language: English
    Keywords: Electronic books. ; Electronic books. ; Electronic books.
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118358887
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118358887
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118358887
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    Online Resource
    HU Berlin
    FU Berlin
  • 7
    Book
    Book
    Basic and advanced Bayesian structural equation modeling : with applications in the medical and behavioral sciences (2012)
    Chichester :Wiley,
    Details
    UID:
    almafu_BV041545481
    Format: XVII, 367 S. : , graph. Darst.
    ISBN: 978-0-470-66952-5
    Series Statement: Wiley series in probability and statistics
    Language: English
    Subjects: Sociology
    RVK:
    MR 2100
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  • 8
    Online Resource
    Online Resource
    Basic and advanced structural equation modeling : with applications in the medical and behavioral sciences (2012)
    Hoboken :Wiley,
    Details
    UID:
    almahu_9948315829002882
    Format: xvii, 367 p. : , ill.
    Edition: Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.
    Series Statement: Wiley series in probability and statistics
    Content: "This book 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"--
    Language: English
    Keywords: Electronic books.
    URL: Click to View
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    HU Berlin
  • 9
    Book
    Book
    An introduction to Korean maritime law (1999)
    Seoul : Korea Univ. Press
    Details
    UID:
    gbv_559509499
    Format: VII, 345 S.
    Edition: 1. ed.
    ISBN: 897641375X
    Series Statement: Inmun sahoe kwahak ch'ongsŏ 29
    Language: English
    Keywords: Korea ; Seerecht
    URL: Inhaltsverzeichnis
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  • 10
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    Book
    Handbook of latent variable and related models (2007)
    Amsterdam : Elsevier North Holland | Oxford : Elsevier Science
    Details
    UID:
    b3kat_BV021832420
    Format: XXII, 435 S. , graph. Darst.
    Edition: 1. ed.
    ISBN: 0444520449 , 9780444520449
    Series Statement: Handbook of computing and statistics with applications 1
    Language: English
    Subjects: Mathematics
    RVK:
    SK 830
    Keywords: Multivariate Analyse ; Statistik ; Datenverarbeitung
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