Latent Variable Modeling And Applications To Causality by Maia BerkaneLatent Variable Modeling And Applications To Causality by Maia Berkane

Latent Variable Modeling And Applications To Causality

EditorMaia Berkane

Paperback | January 24, 1997

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This volume gathers refereed papers presented at the 1994 UCLA conference on "Latent Variable Modeling and Application to Causality." The papers in this volume are representative of a wide variety of disciplines in which the use of latent varible models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Modela and Causal Reasoning, and the chapters are innovations from contributions in disciplines not traditionally associated with the behavorial sciences, such as computer science and public health. The second section encompasses new approaches to questions of model selection with an emphasis on factor analysis and time varying systems. All the chapters present new results not published elsewhere.
Title:Latent Variable Modeling And Applications To CausalityFormat:PaperbackDimensions:292 pages, 9.25 × 6.1 × 0.01 inPublished:January 24, 1997Publisher:Springer New York

The following ISBNs are associated with this title:

ISBN - 10:0387949178

ISBN - 13:9780387949178

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Table of Contents

Contents: 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.- 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 parametrization.- 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.