Variable selection in nonparametric additive models

TL;DR

This paper develops a method using adaptive group Lasso for variable selection in high-dimensional nonparametric additive models, accurately identifying relevant components with theoretical guarantees and practical effectiveness.

Contribution

It introduces an adaptive group Lasso approach for variable selection in high-dimensional additive models, with proven consistency and optimal convergence rates.

Findings

The method correctly identifies nonzero components with high probability.

Simulation results demonstrate good performance with moderate sample sizes.

Application to real data illustrates practical utility.

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.