A0207
Title: A subsampling method for regression problems based on minimum energy criterion
Authors: Wenlin Dai - Renmin University of China (China) [presenting]
Abstract: The extraordinary amounts of data generated in science today pose heavy demands on computational resources and time, which hinders the implementation of various statistical methods. An efficient and popular strategy of downsizing data volumes and hence alleviating these challenges is subsampling. However, the existing methods either rely on specific assumptions for the underlying models or acquire only partial information from the available data. We propose a novel approach, termed adaptive subsampling, that is based on the minimum energy criterion (ASMEC). The proposed method requires no explicit model assumptions and `smartly' incorporates information on covariates and responses. ASMEC subsamples possess two desirable properties: space-filling and spatial adaptiveness to the full data. We investigate the theoretical properties of the ASMEC estimator under the smoothing spline regression model and show that it converges at an identical rate to two recently proposed basis selection methods. The effectiveness and robustness of the ASMEC approach are also supported by a variety of simulated examples and two real-life examples.
