Variable selection in nonparametric additive models

Jian Huang*, Joel L. Horowitz, Fengrong Wei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

251 Scopus citations

Abstract

We consider a nonparametric additive model of a conditional mean function in which the number of variables and additive components may be larger than the sample size but the number of nonzero additive components is "small" relative to the sample size. The statistical problem is to determine which additive components are nonzero. The additive components are approximated by truncated series expansions with B-spline bases. With this approximation, the problem of component selection becomes that of selecting the groups of coefficients in the expansion. We apply the adaptive group Lasso to select nonzero components, using the group Lasso to obtain an initial estimator and reduce the dimension of the problem. We give conditions under which the group Lasso selects a model whose number of components is comparable with the underlying model, and the adaptive group Lasso selects the nonzero components correctly with probability approaching one as the sample size increases and achieves the optimal rate of convergence. The results of Monte Carlo experiments show that the adaptive group Lasso procedure works well with samples of moderate size. A data example is used to illustrate the application of the proposed method.

Original languageEnglish (US)
Pages (from-to)2282-2313
Number of pages32
JournalAnnals of Statistics
Volume38
Issue number4
DOIs
StatePublished - Aug 2010

Keywords

  • Adaptive group lasso
  • Component selection
  • High-dimensional data
  • Nonparametric regression
  • Selection consistency

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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