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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| Other Authors: | |
| Format: | eBook |
| Language: | English |
| Published: |
Amsterdam ; Boston :
Elsevier/North-Holland,
2007.
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| Edition: | 1st ed. |
| Series: | Handbook of computing and statistics with applications.
v. 1. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book Publisher description |
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| 245 | 0 | 0 | |a Handbook of latent variable and related models / |c edited by Sik-Yum Lee. |
| 250 | |a 1st ed. | ||
| 264 | 1 | |a Amsterdam ; |a Boston : |b Elsevier/North-Holland, |c 2007. | |
| 300 | |a xxii, 435 pages : |b illustrations ; |c 25 cm. | ||
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| 490 | 1 | |a Handbook of computing and statistics with applications, |x 1871-0301 ; |v v. 1 | |
| 533 | |a Electronic reproduction. |b Amsterdam : |c Elsevier Science & Technology, |d 2007. |n Mode of access: World Wide Web. |n System requirements: Web browser. |n Title from title screen (viewed on July 25, 2007). |n Access may be restricted to users at subscribing institutions. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a 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). | |
| 504 | |a Includes bibliographical references and indexes. | ||
| 500 | |a Electronic resource. | ||
| 650 | 0 | |a Latent structure analysis. | |
| 650 | 0 | |a Latent variables. | |
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| 700 | 1 | |a Lee, Sik-Yum. | |
| 710 | 2 | |a ScienceDirect (Online service) | |
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