Latent variables and the network perspective

Latent variables in econometrics

Unobservable variables in econometrics are represented in one of three ways: by variables contaminated by measurement errors, by proxy variables, or by various manifest indicators and/or causes. This paper contains a discussion of models involving each of these representations, and highlights certain interesting implications that have been ‘insufficiently emphasized or completely unrecognized i...

The theoretical status of latent variables.

This article examines the theoretical status of latent variables as used in modern test theory models. First, it is argued that a consistent interpretation of such models requires a realist ontology for latent variables. Second, the relation between latent variables and their indicators is discussed. It is maintained that this relation can be interpreted as a causal one but that in measurement ...

Latent Variables, State Spaces, and Mixing

We argue that many models for multivariate longitudinal and cross-sectional data analysis have a common ancestry. They all are based on the qualitative idea that if we knew the actual state of the world, the relations between the observed quantities would be truly simple. This is shown to lead directly to factor analysis, IRT, state space models, mixture densities, latent Markov chains, MIMIC, ...

Object and Action Classification with Latent Variables

In this paper we address the problem of classifying objects (e.g. person or car) and actions (e.g. hugging or eating) [2]. The more successful methods are based on a uniform pyramidal representation (SPM) built on a visual word vocabulary [1]. In this paper, we augment the classification by adding more flexible spatial information. This will be formulated more generally as inferring additional ...

Latent Variables, Causal Models, and Overidentifying Constraints