Ontology highlight
ABSTRACT:
SUBMITTER: Yu T
PROVIDER: S-EPMC6309280 | biostudies-literature | 2018 Aug
REPOSITORIES: biostudies-literature
Statistical analysis and data mining 20180619 4
We present a method of variable selection for the sparse generalized additive model. The method doesn't assume any specific functional form, and can select from a large number of candidates. It takes the form of incremental forward stagewise regression. Given no functional form is assumed, we devised an approach termed "roughening" to adjust the residuals in the iterations. In simulations, we show the new method is competitive against popular machine learning approaches. We also demonstrate its ...[more]