Latent variable model

About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.

Modeling annotated data

Abstract: We consider the problem of modeling annotated data---data with multiple types where the instance of one type (such as a caption) serves as a description of the other type (such as an image). We describe three hierarchical probabilistic mixture models which aim to describe such data, culminating in correspondence latent Dirichlet allocation, a latent variable model that is effective at modeling the joint distribution of both types and the conditional distribution of the annotation given the primary type. We conduct experiments on the Corel database of images and captions, assessing performance in terms of held-out likelihood, automatic annotation, and text-based image retrieval.

The theoretical status of latent variables

Abstract: 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 models for interindividual differences the relation does not apply to the level of the individual person. To substantiate intraindividual causal conclusions, one must explicitly represent individual level processes in the measurement model. Several research strategies that may be useful in this respect are discussed, and a typology of constructs is proposed on the basis of this analysis. The need to link individual processes to latent variable models for interindividual differences is emphasized.

Basics of structural equation modeling

Abstract: PART ONE: BACKGROUND What Does It Mean to Model Hypothesized Causal Processes with Nonexperimental Data? History and Logic of Structural Equation Modeling PART TWO: BASIC APPROACHES TO MODELING WITH SINGLE OBSERVED MEASURES OF THEORETICAL VARIABLES The Basics Path Analysis and Partitioning of Variance Effects of Collinearity on Regression and Path Analysis Effects of Random and Nonrandom Error on Path Models Recursive and Longitudinal Models Where Causality Goes in More Than One Direction and Where Data Are Collected Over Time PART THREE: FACTOR ANALYSIS AND PATH MODELING Introducing the Logic of Factor Analysis and Multiple Indicators to Path Modeling PART FOUR: LATENT VARIABLE STRUCTURAL EQUATION MODELS Putting It All Together Latent Variable Structural Equation Modeling Using Latent Variable Structural Equation Modeling to Examine Plausability of Models Logic of Alternative Models and Significance Tests Variations on the Basic Latent Variable Structural Equation Model Wrapping up

Latent variable models and factor analysis

Abstract: Basic ideas foundations factor analysis and latent profile analysis latent class and latent trait analysis - binary response data, polysomous response data mixed response variables posterior analysis linear structural relations methods comparison with principle components analysis synoptic view.