An asymptotic and empirical smoothing parameters selection method for smoothing spline ANOVA models in large samples.

ABSTRACT: Large samples are generated routinely from various sources. Classic statistical models, such as smoothing spline ANOVA models, are not well equipped to analyse such large samples because of high computational costs. In particular, the daunting computational cost of selecting smoothing parameters renders smoothing spline ANOVA models impractical. In this article, we develop an asympirical, i.e., asymptotic and empirical, smoothing parameters selection method for smoothing spline ANOVA models in large samples. The idea of our approach is to use asymptotic analysis to show that the optimal smoothing parameter is a polynomial function of the sample size and an unknown constant. The unknown constant is then estimated through empirical subsample extrapolation. The proposed method significantly r

SUBMITTER: Sun X 

PROVIDER: S-EPMC8294473 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature