Skip to main content
SpringerLink
Log in

Latent Variable Modeling and Applications to Causality

  • Conference proceedings
  • © 1997

Overview

Editors:
  1. Maia Berkane

    1. Department of Mathematics, Harvard University, Cambridge, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

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

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (14 papers)

  1. Front Matter

    Pages i-vii
    Download chapter PDF

  2. Embedding common factors in a path model

    • Roderick P. McDonald
    Pages 1-10

  3. Measurement, Causation and Local Independence in Latent Variable Models

    • Michael E. Sobel
    Pages 11-28

  4. On the Identification of Nonparametric Structural Models

    • Judea Pearl
    Pages 29-68

  5. Causal Inference from Complex Longitudinal Data

    • James M. Robins
    Pages 69-117

  6. Models as Instruments, With Applications to Moment Structure Analysis

    • Jan De Leeuw
    Pages 119-131

  7. Bias and mean square error of the maximum likelihood estimators of the parameters of the intraclass correlation model

    • Maia Berkane, Peter Bentler
    Pages 133-147

  8. Latent Variable Growth Modeling with Multilevel Data

    • Bengt MuthĂ©n
    Pages 149-161

  9. High-dimensional Full-information Item Factor Analysis

    • R. Darrell Bock, Steven Schilling
    Pages 163-176

  10. Dynamic Factor Models for the Analysis of Ordered Categorical Panel Data

    • Gerhard Arminger
    Pages 177-194

  11. Model fitting procedures for nonlinear factor analysis using the errors-in-variables parameterization

    • Yasuo Amemiya, Ilker Yalcin
    Pages 195-210

  12. Multivariate Regression with Errors in Variables: Issues on Asymptotic Robustness

    • Albert Satorra
    Pages 211-228

  13. Non-Iterative Fitting of the Direct Product Model for Multitrait-Multimethod Matrices

    • M. W. Browner, H. F. Strydom
    Pages 229-245

  14. An EM Algorithm for ML Factor Analysis with Missing Data

    • Mortaza Jamshidian
    Pages 247-258

  15. Optimal Conditionally Unbiased Equivariant Factor Score Estimators

    • Peter M. Bentler, Ke-Hai Yuan
    Pages 259-281

  16. Back Matter

    Pages 283-284
    Download chapter PDF
Back to top

Keywords

About this book

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.

Editors and Affiliations

  • Department of Mathematics, Harvard University, Cambridge, USA

    Maia Berkane

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation