Variable selection in nonparametric additive models

  • Huang J
  • Horowitz J
  • Wei F
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Abstract

We consider a nonparametric additive model of a conditional mean function in which the number of variables and additive compo-nents may be larger than the sample size but the number of nonzero additive components is " small " relative to the sample size. The statis-tical problem is to determine which additive components are nonzero. The additive components are approximated by truncated series ex-pansions with B-spline bases. With this approximation, the problem of component selection becomes that of selecting the groups of co-efficients in the expansion. We apply the adaptive group Lasso to select nonzero components, using the group Lasso to obtain an ini-tial estimator and reduce the dimension of the problem. We give conditions under which the group Lasso selects a model whose num-ber 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 experi-ments 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.

Author-supplied keywords

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

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