Latent Variable Modelling: A Survey
Latent variable modelling has gradually become an integral part of mainstream statistics and is currently used for a multitude of applications in different subject areas. Examples of 'traditional' latent variable models include latent class models, item-response models, common factor models, structural equation models, mixed or random effects models and covariate measurement error models. Although latent variables have widely different interpretations in different settings, the models have a very similar mathematical structure. This has been the impetus for the formulation of general modelling frameworks which accommodate a wide range of models. Recent developments include multilevel structural equation models with both continuous and discrete latent variables, multiprocess models and nonlinear latent variable models. Copyright 2007 Board of the Foundation of the Scandinavian Journal of Statistics..
Year of publication: |
2007
|
---|---|
Authors: | SKRONDAL, ANDERS ; RABE-HESKETH, SOPHIA |
Published in: |
Scandinavian Journal of Statistics. - Danish Society for Theoretical Statistics, ISSN 0303-6898. - Vol. 34.2007, 4, p. 712-745
|
Publisher: |
Danish Society for Theoretical Statistics Finnish Statistical Society Norwegian Statistical Association Swedish Statistical Association |
Saved in:
freely available
Saved in favorites
Similar items by person
-
Maximum likelihood estimation of generalized linear models with covariate measurement error
Rabe-Hesketh, Sophia, (2003)
-
Rabe-Hesketh, Sophia, (2005)
-
Avoiding biased versions of Wooldridge's simple solution to the initial conditions problem
Rabe-Hesketh, Sophia, (2013)
- More ...