Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1152349 | Statistics & Probability Letters | 2012 | 7 Pages |
Abstract
In this short paper, we demonstrate that the popular penalized estimation method typically used for variable selection in parametric or semiparametric models can actually provide a way to identify linear components in additive models. Unlike most studies in the literature, we are NOT performing variable selection. Due to the difficulty in a priori deciding which predictors should enter the partially linear additive model as the linear components, such a method will prove useful in practice.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Heng Lian,