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Activity Number: 646 - Experimental Design Thinking for Big Data
Type: Invited
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #326847
Title: Information-Based Subdata Selection
Author(s): John Stufken*
Companies: Arizona State University
Keywords: Big data; Sampling-based methods; Optimal design of experiments; D-optimality; Fisher information

Simply due to size, in order to analyze a huge data set, it may be necessary or desirable to perform the analysis on selected subdata. There are various methods for selecting subdata from big data, including sampling-based methods and methods that advocate the use of information-based criteria. The information-based criteria relate the problem of ``optimal'' subdata selection to the problem of optimal design of experiments. While there are significant differences between the two problems, the connection makes tools from optimal design available for subdata selection problems. We introduce the basic ideas, demonstrate the success of information-based methods, and discuss some of the shortcomings.

Authors who are presenting talks have a * after their name.

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