Latent Variable Modeling and Applications to Causality

  • Maia Berkane
Conference proceedings

Part of the Lecture Notes in Statistics book series (LNS, volume 120)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Roderick P. McDonald
    Pages 1-10
  3. R. Darrell Bock, Steven Schilling
    Pages 163-176
  4. Peter M. Bentler, Ke-Hai Yuan
    Pages 259-281
  5. Back Matter
    Pages 283-284

About these proceedings


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.


Estimator Factor analysis Fitting Latent variable model Likelihood best fit correlation expectation–maximization algorithm

Editors and affiliations

  • Maia Berkane
    • 1
  1. 1.Department of MathematicsHarvard UniversityCambridgeUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 1997
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-94917-8
  • Online ISBN 978-1-4612-1842-5
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site