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Handbook of latent variable and related models /

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 spect...

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Bibliographic Details
Corporate Authors: Elsevier Science & Technology.
Group Author: Lee, Sik-Yum. (Editor)
Published: Elsevier/North-Holland,
Publisher Address: Amsterdam ; Boston :
Publication Dates: 2007.
Literature type: eBook
Language: English
Edition: First edition.
Series: Handbook of computing and statistics with applications, v. 1
Subjects:
Latent structure analysis.
Latent variables.
MATHEMATICS > Multivariate Analysis. > Multivariate Analysis.
Electronic books.
Online Access: http://www.sciencedirect.com/science/book/9780444520449
Summary: 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.
Carrier Form: 1 online resource (xxii, 435 pages) : illustrations.
Bibliography: Includes bibliographical references and indexes.
ISBN: 9780444520449
0444520449
9780080471266
0080471269
Index Number: QA278
CLC: O212.4
Contents: 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 So an) -- 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).

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