Latent Variable Modeling and Applications to Causality (eBook, PDF)

Redaktion: Berkane, Maia

• Format: PDF

Produktbeschreibung

Latent variable modeling has many applications to the behavioral sciences. This refereed proceedings of a 1994 conference at UCLA includes applications to this area as well as newer applications to computer science and public health. All papers present new results not published elsewhere.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 284
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461218425
• Artikelnr.: 44042665

Inhaltsangabe

Causality and Path Models.- Embedding Common factors in a Path Model.- Measurement, Causation and Local Independence in Latent Variable Models.- On the Identifiability of Nonparametric Structural Models.- Estimating the Causal effects of Time Varying Endogeneous Treatments by G-Estimation of Structural Nested Models.- Latent Variables.- Model as Instruments, with Applications to Moment Structure Analysis.- Bias and Mean Square Error of the Maximum Likelihood Estimators of the Parameters of the Intraclass Correlation Model.- Latent Variable Growth Modeling with Multilevel Data.- High-Dimensional Full-Information Item Factor Analysis.- Dynamic Factor Models for the Analysis of Ordered Categorical Panel data.- Model Fitting Procedures for Nonlinear Factor Analysis Using the Errors-in-Variables Parameterization.- Multivariate Regression with Errors in Variables: Issues on Asymptotic Robustness.- Non-Iterative fitting of the Direct Product Model for Multitrait-Multimethod Correlation Matrices.- An EM Algorithm for ML Factor Analysis with Missing Data.- Optimal Conditionally Unbiased Equivariant Factor Score Estimators.