A1080
Title: Sampling big data for model building using dimension reduction
Authors: Ching-Chi Yang - University of Memphis (United States) [presenting]
Abstract: Handling the extraordinary data volume generated in many fields is challenging with current computational resources and techniques, especially when applying conventional statistical methods to big data. A common approach is to select sub-data that represent the full data. However, the sub-data should be carefully selected based on different objectives, such as rare event detection. Recent developments and newly published methods are introduced, and a broad discussion on potential research opportunities in the design of experiments is initiated.
