Latent Variable Modelling: A Survey *

By A. Skrondal, Sophia Rabe-Hesketh. 2007

157 citations recorded by the source corpus.

Abstract. 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.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/57bdb4a3c387c016d0278c95cda3e79d221dd590

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In Computer Networks and Communications

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Prediction in multilevel generalized linear models (2009, background): “See Skrondal and Rabe-Hesketh (2007b) for a recent review discussing the relationships between these and other models.”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “sing a ﬁxed transformation, and stochastic variations are known as latent variable models where the distribution in higher dimension is due to small number of hidden variables acting in a combination [87]. These models are used for data visualization, dimensionality reduction, optimization, distribu”