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We present a novel method for variable selection in regression models whencovariates are measured with error. The iterative algorithm we propose,MEBoost, follows a path defined by estimating equations that correct forcovariate measurement error. Via simulation, we evaluated our method andcompare its performance to the recently-proposed Convex Conditioned Lasso(CoCoLasso) and to the "naive" Lasso which does not correct for measurementerror. Increasing the degree of measurement error increased prediction errorand decreased the probability of accurate covariate selection, but this loss ofaccuracy was least pronounced when using MEBoost. We illustrate the use ofMEBoost in practice by analyzing data from the Box Lunch Study, a clinicaltrial in nutrition where several variables are based on self-report and hencemeasured with error.

MEBoost: Variable Selection in the Presence of Measurement Error