Similar to variable selection in the linear regression model, selecting
significant components in the popular additive regression model is of great
interest. However, such components are unknown smooth functions of independent
variables, which are unobservable. As such, some approximation is needed. In
this paper, we suggest a combination of penalized regression spline
approximation and group variable selection, called the lasso-type spline method
(LSM), to handle this component selection problem with a diverging number of
strongly correlated variables in each group.