Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models.

ABSTRACT: Partially linear models provide a useful class of tools for modeling complex data by naturally incorporating a combination of linear and nonlinear effects within one framework. One key question in partially linear models is the choice of model structure, that is, how to decide which covariates are linear and which are nonlinear. This is a fundamental, yet largely unsolved problem for partially linear models. In practice, one often assumes that the model structure is given or known and then makes estimation and inference based on that structure. Alternatively, there are two methods in common use for tackling the problem: hypotheses testing and visual screening based on the marginal fits. Both methods are quite useful in practice but have their drawbacks. First, it is difficult to construct

SUBMITTER: Zhang HH 

PROVIDER: S-EPMC3222957 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature