A Bayesian mixture of experts approach to covariate misclassification

Author ORCID Identifier

Michelle Xia:https://orcid.org/0000-0002-5137-2002

Publication Title

Canadian Journal of Statistics

ISSN

03195724

E-ISSN

44166

Document Type

Article

Abstract

This article considers misclassification of categorical covariates in the context of regression analysis; if unaccounted for, such errors usually result in mis-estimation of model parameters. With the presence of additional covariates, we exploit the fact that explicitly modelling non-differential misclassification with respect to the response leads to a mixture regression representation. Under the framework of mixture of experts, we enable the reclassification probabilities to vary with other covariates, a situation commonly caused by misclassification that is differential on certain covariates and/or by dependence between the misclassified and additional covariates. Using Bayesian inference, the mixture approach combines learning from data with external information on the magnitude of errors when it is available. In addition to proving the theoretical identifiability of the mixture of experts approach, we study the amount of efficiency loss resulting from covariate misclassification and the usefulness of external information in mitigating such loss. The method is applied to adjust for misclassification on self-reported cocaine use in the Longitudinal Studies of HIV-Associated Lung Infections and Complications.

First Page

731

Last Page

750

Publication Date

12-1-2020

DOI

10.1002/cjs.11560

Keywords

Bayesian inference, covariate misclassification, identifiability, Markov chain Monte Carlo, mixture of experts

Department

Department of Statistics and Actuarial Science

Link to Full Text

Share

COinS