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＜電子ブック＞
Latent Variable Modeling and Applications to Causality / edited by Maia Berkane
(Lecture Notes in Statistics. ISSN:21977186 ; 120)

版	1st ed. 1997.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	1997
大きさ	VIII, 284 p : online resource
著者標目	Berkane, Maia editor SpringerLink (Online service)
件　名	LCSH:Probabilities LCSH:Mathematics LCSH:Mathematical models FREE:Probability Theory FREE:Applications of Mathematics FREE:Mathematical Modeling and Industrial Mathematics
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-1-4612-1842-5

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