Latent Variable Modeling and Applications to Causality (Lecture Notes in Statistics, 120)

ISBN 10: 0387949178 / ISBN 13: 9780387949178

Published by Springer, 1997

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Bibliographic Details

• Title: Latent Variable Modeling and Applications to Causality (Lecture Notes in Statistics, 120)
• Publisher: Springer
• Publication Date: 1997
• Language: English
• ISBN 10: 0387949178
• ISBN 13: 9780387949178
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

Synopsis

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.