Penalized partially linear models using sparse representations with an application to fMRI time series
Penalized partially linear models using sparse representations with an application to fMRI time series
In this paper, we consider modeling the nonparametric component in partially linear models (PLMs) using linear sparse representations, e.g., wavelet expansions. Two types of representations are investigated, namely, orthogonal bases (complete) and redundant overcomplete expansions. For bases, we introduce a regularized estimator of the nonparametric part. The important contribution here …