Collates papers from a 1994 UCLA conference on "Latent Variable Modeling and Application to Causality". The first part covers path modela and causal reasoning, while the second focuses on approaches to questions of model selection with an emphasis on factor analysis and time varying systems. Editor(s): Berkane, M. (University of Cambridge, Massachusetts, USA). Series: Lecture Notes in Statistics. Num Pages: 292 pages, biography. BIC Classification: JHBC; PBT; PBWH. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 233 x 156 x 17. Weight in Grams: 428. . 1997. Softcover reprint of the original 1st ed. 1997. Paperback. . . . . Books ship from the US and Ireland.

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Titel: Latent Variable Modeling and Applications to Causality
Verlag: Springer-Verlag New York Inc.
Erscheinungsjahr: 1997
Sprache: Englisch
ISBN-10: 0387949178
ISBN-13: 9780387949178
Einband: Softcover
Zustand: New

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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.

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