A general algorithm for error-in-variables regression modelling using Monte Carlo expectation maximization.

ABSTRACT: In regression modelling, measurement error models are often needed to correct for uncertainty arising from measurements of covariates/predictor variables. The literature on measurement error (or errors-in-variables) modelling is plentiful, however, general algorithms and software for maximum likelihood estimation of models with measurement error are not as readily available, in a form that they can be used by applied researchers without relatively advanced statistical expertise. In this study, we develop a novel algorithm for measurement error modelling, which could in principle take any regression model fitted by maximum likelihood, or penalised likelihood, and extend it to account for uncertainty in covariates. This is achieved by exploiting an interesting property of the Monte Carlo Exp

SUBMITTER: Stoklosa J 

PROVIDER: S-EPMC10069785 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature