hdme: High-Dimensional Regression with Measurement Error
This paper presents the hdme package, implementing variable selection methods for regression with covariate measurement error.
Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error
This paper proposes an extension of the generalized Dantzig selector for cases with measurement error in the predictor variables.
hdme: High-Dimensional Regression with Measurement Error
This R package contains functions for fitting variable selection models in the presence of noise in the predictor variables. In particular, it supports a corrected lasso and the generalized matrix uncertainty selector. In addition, it offers an implementation of the (generalized) Dantzig selector.
Measurement Error in Lasso: Impact and Likelihood Bias Correction
This paper analyzes the impact of covariate measurement error in the lasso method for penalized regression, and then proposes correction methods both for linear models and generalized linear models.