Latent Variable Modeling and Applications to Causality (Paperback)

Maia Berkane

ISBN 10: 0387949178 / ISBN 13: 9780387949178

Editore: Springer-Verlag New York Inc., New York, NY, 1997

Riguardo questo articolo

Paperback. Collates papers presented at an 1994 UCLA conference on "Latent Varaible Modeling and Application to Causality". The selection of the papers aims to be representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The first section covers path models and causal reasoning, and the chapters are innovations from contributions in disciplines not traditionally associated with the behavioural sciences, such as computer and public health. The second section encompasses new approaches to questions of model selection with an emphasis on factor analysis and time varying systems. This volume gathers refereed papers presented at the 1994 UCLA conference on "La tent Variable Modeling and Application to Causality. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi nal latent variables. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9780387949178

Dati bibliografici

• Titolo: Latent Variable Modeling and Applications to Causality (Paperback)
• Editore: Springer-Verlag New York Inc., New York, NY
• Data di pubblicazione: 1997
• Lingua: Inglese
• ISBN 10: 0387949178
• ISBN 13: 9780387949178
• Rilegatura: Paperback
• Edizione: prima edizione
• Condizione: new

Descrizione articolo

Riassunto

Collates papers presented at an 1994 UCLA conference on "Latent Varaible Modeling and Application to Causality". The selection of the papers aims to be representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The first section covers path models and causal reasoning, and the chapters are innovations from contributions in disciplines not traditionally associated with the behavioural sciences, such as computer and public health. The second section encompasses new approaches to questions of model selection with an emphasis on factor analysis and time varying systems.

Contenuti

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.