|Title||Automatic structure recovery for additive models.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Wu, Yichao, and Leonard A. Stefanski|
|Date Published||2015 Jun 02|
We propose an automatic structure recovery method for additive models, based on a backfitting algorithm coupled with local polynomial smoothing, in conjunction with a new kernel-based variable selection strategy. Our method produces estimates of the set of noise predictors, the sets of predictors that contribute polynomially at different degrees up to a specified degree , and the set of predictors that contribute beyond polynomially of degree . We prove consistency of the proposed method, and describe an extension to partially linear models. Finite-sample performance of the method is illustrated via Monte Carlo studies and a real-data example.
|Original Publication||Automatic structure recovery for additive models.|
|PubMed Central ID||PMC4487890|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA149569 / CA / NCI NIH HHS / United States
Automatic structure recovery for additive models.