SCAD-penalised generalised additive models with non-polynomial dimensionality

Abstract

In this article, we study the (group) smoothly clipped absolute deviation (SCAD) estimator in the estimation of generalised additive models. The SCAD penalty, proposed by Fan and Li [(2001) ‘Variable Selection via Nonconcave Penalised Likelihood and Its Oracle Properties’, Journal of the American Statistical Association 96(456), 1348–1360], has many desirable properties including continuity, sparsity and unbiasedness. For high-dimensional parametric models, it has only recently been shown that the SCAD estimator can deal with problems with dimensions much larger than the sample size. Here, we show that the SCAD estimator can be successfully applied to generalised additive models with non-polynomial dimensionality and our study represents the first such result for the SCAD estimator in nonparametric problems, as far as we know. In particular, under suitable assumptions, we theoretically show that the dimension of the problem can be close to exp<texlscub>n d/(2d+1)</texlscub>, where n is the sample size and d is the smoothness parameter of the component functions. Some Monte Carlo studies and a real data application are also presented.