Latent Variable Modeling and Applications to Causality

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9780387949178: Latent Variable Modeling and Applications to Causality

This volume gathers refereed papers presented at the 1994 UCLA conference on "La- tent Variable Modeling and Application to Causality. " The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri- butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi- tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi- nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

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.

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Editore: Springer-Verlag New York Inc., United States (1997)
ISBN 10: 0387949178 ISBN 13: 9780387949178
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Descrizione libro Springer-Verlag New York Inc., United States, 1997. Paperback. Condizione libro: New. 231 x 155 mm. Language: English . Brand New Book ***** Print on Demand *****.This volume gathers refereed papers presented at the 1994 UCLA conference on La- tent Variable Modeling and Application to Causality. The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri- butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi- tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi- nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data. Softcover reprint of the original 1st ed. 1997. Codice libro della libreria AAV9780387949178

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Descrizione libro Springer, 1997. Paperback. Condizione libro: NEW. 9780387949178 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Codice libro della libreria HTANDREE0411230

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Editore: Springer-Verlag New York Inc., United States (1997)
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Descrizione libro Springer-Verlag New York Inc., United States, 1997. Paperback. Condizione libro: New. 231 x 155 mm. Language: English . Brand New Book ***** Print on Demand *****. This volume gathers refereed papers presented at the 1994 UCLA conference on La- tent Variable Modeling and Application to Causality. The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri- butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi- tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi- nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data. Softcover reprint of the original 1st ed. 1997. Codice libro della libreria AAV9780387949178

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Descrizione libro Springer-Verlag New York Inc., 1997. PAP. Condizione libro: New. New Book. Delivered from our UK warehouse in 3 to 5 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice libro della libreria LQ-9780387949178

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Descrizione libro Springer, 1997. Paperback. Condizione libro: New. book. Codice libro della libreria 0387949178

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Descrizione libro Springer, 1997. Condizione libro: New. This item is printed on demand for shipment within 3 working days. Codice libro della libreria LP9780387949178

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Descrizione libro Springer, 1997. Paperback. Condizione libro: New. Softcover reprint of the origina. This item is printed on demand. Codice libro della libreria DADAX0387949178

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