Group LASSO for high-dimensional partially linear errors-invariables models

This article focuses on group variable selection for high dimensional partially linear models when the covariates are measured with additive errors. We apply the group least absolute and shrinkage operator (LASSO) penalty to simultaneously estimate and select significant variables. Finite sample performance of the proposed procedure is assessed by simulation studies, where we compare the naive and bias-corrected group LASSO estimators

کلیدواژه ها:

Adaptive group LASSO ، Measurement errors ، Partially linear model