Work

A Note on the Use of Mixture Models for Individual Prediction

Year: 2016

Type: article

Abstract: Mixture models capture heterogeneity in data by decomposing the population into latent subgroups, each of which is governed by its own subgroup-specific set of parameters. Despite the flexibility and widespread use of these models, most applications have focused solely on making inferences for whole or sub-populations, rather than individual cases. The current article presents a general framework for computing marginal and conditional predicted values for individuals using mixture model results. These predicted values can be used to characterize covariate effects, examine the fit of the model for specific individuals, or forecast future observations from previous ones. Two empirical examples are provided to demonstrate the usefulness of individual predicted values in applications of mixture models. The first example examines the relative timing of initiation of substance use using a multiple event process survival mixture model whereas the second example evaluates changes in depressive symptoms over adolescence using a growth mixture model.

Source: Structural Equation Modeling A Multidisciplinary Journal

DOI: 10.1080/10705511.2016.1168266

Authors: Veronica T. Cole, Daniel J. Bauer

Institution: University of North Carolina at Chapel Hill

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute on Drug Abuse

Awards: Mixture Modeling of Patterns of Substance Use: Taking into Account Measurement Bias, G5695331249, Harmonizing substance use and disorder measures to facilitate multistudy analyses