B0465
Title: TreeSS: A model-free Tree-based subdata selection method for prediction
Authors: John Stufken - George Mason University (United States) [presenting]
Rakhi Singh - Binghamton University (United States)
Abstract: With ever-larger datasets, there is a growing need for methods that select just a small portion of the entire dataset (subdata) so that reliable inferences can be obtained by analyzing only the selected subdata. Many of the subdata selection methods that have been proposed in recent years are based on model assumptions for the data. While these methods can work extremely well when the model assumptions hold, they may yield poor results if the assumptions are wrong. In addition, subdata that is good for one task may not be so good for another. A model-free tree-based subdata selection method (TreeSS) is introduced and discussed that focuses on selecting subdata that perform well for prediction.
