  In the current era of big data, researchers routinely collect and analyze
data of super-large sample sizes. Data-oriented statistical methods have been
developed to extract information from super-large data. Smoothing spline ANOVA
(SSANOVA) is a promising approach for extracting information from noisy data;
however, the heavy computational cost of SSANOVA hinders its wide application.
In this paper, we propose a new algorithm for fitting SSANOVA models to
super-large sample data. In this algorithm, we introduce rounding parameters to
make the computation scalable. To demonstrate the benefits of the rounding
parameters, we present a simulation study and a real data example using
electroencephalography data. Our results reveal that (using the rounding
parameters) a researcher can fit nonparametric regression models to very large
samples within a few seconds using a standard laptop or tablet computer.
