Variable selection in partially linear wavelet models

Variable selection is fundamental in high-dimensional statistical modelling, including non- and semiparametric regression. However, little work has been done for variable selection in a partially linear model (PLM). We propose and study a unified approach via double penalized least squares, retaining good features of both variable selection and model estimation in the framework of PLM. The proposed method is distinguished from others in that the penalty functions combine the l(1) penalty coming from wavelet thresholding in the non-parametric component with the l(1) penalty from the lasso in the parametric component. Simulations are used to investigate the performances of the proposed estimator in various settings, illustrating its effectiveness for simultaneous variable selection as well as estimation.