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Latent Variable Modeling and Applications to Causality

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Cover of 'Latent Variable Modeling and Applications to Causality'

Table of Contents

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    Book Overview
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    Chapter 1 Embedding common factors in a path model
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    Chapter 2 Measurement, Causation and Local Independence in Latent Variable Models
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    Chapter 3 On the Identification of Nonparametric Structural Models
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    Chapter 4 Causal Inference from Complex Longitudinal Data
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    Chapter 5 Models as Instruments, With Applications to Moment Structure Analysis
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    Chapter 6 Bias and mean square error of the maximum likelihood estimators of the parameters of the intraclass correlation model
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    Chapter 7 Latent Variable Growth Modeling with Multilevel Data
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    Chapter 8 High-dimensional Full-information Item Factor Analysis
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    Chapter 9 Dynamic Factor Models for the Analysis of Ordered Categorical Panel Data
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    Chapter 10 Model fitting procedures for nonlinear factor analysis using the errors-in-variables parameterization
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    Chapter 11 Multivariate Regression with Errors in Variables: Issues on Asymptotic Robustness
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    Chapter 12 Non-Iterative Fitting of the Direct Product Model for Multitrait-Multimethod Matrices
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    Chapter 13 An EM Algorithm for ML Factor Analysis with Missing Data
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    Chapter 14 Optimal Conditionally Unbiased Equivariant Factor Score Estimators
Attention for Chapter 1: Embedding common factors in a path model
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Chapter title
Embedding common factors in a path model
Chapter number 1
Book title
Latent Variable Modeling and Applications to Causality
Published by
Springer, New York, NY, January 1997
DOI 10.1007/978-1-4612-1842-5_1
Book ISBNs
978-0-387-94917-8, 978-1-4612-1842-5
Authors

Roderick P. McDonald

View on publisher site

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