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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
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 > Probability & Statistics > 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 L

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