Smoothing splines approximation using Hilbert curve basis selection.

ABSTRACT: Smoothing splines have been used pervasively in nonparametric regressions. However, the computational burden of smoothing splines is significant when the sample size n is large. When the number of predictors d ≥ 2 , the computational cost for smoothing splines is at the order of O(n ³) using the standard approach. Many methods have been developed to approximate smoothing spline estimators by using q basis functions instead of n ones, resulting in a computational cost of the order O(nq ²). These methods are called the basis selection methods. Despite algorithmic benefits, most of the basis selection methods require the assumption that the sample is uniformly-distributed on a hyper-cube. These methods may have dete

SUBMITTER: Meng C 

PROVIDER: S-EPMC9674117 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature