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Principles of Experimental Design for Big Data Analysis.

Christopher C Drovandi1, Christopher Holmes2, James M McGree1

  • 1School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia, 4000.

Statistical Science : a Review Journal of the Institute of Mathematical Statistics
|September 9, 2017
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This paper explores using optimal experimental design methods for analyzing Big Data. It proposes retrospective designed sampling to improve Big Data analysis and calls for collaboration between optimization and experimental design communities.

Keywords:
active learningbig datadimension reductionexperimental designsub-sampling

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Area of Science:

  • Data Science
  • Statistics
  • Experimental Design

Background:

  • Big Data presents significant analysis challenges due to size, heterogeneity, and quality issues.
  • Traditional optimal experimental design methods are prospective, limiting their application to retrospective Big Data analysis.

Purpose of the Study:

  • To explore the potential of decision theoretic optimal experimental design for Big Data analysis.
  • To introduce retrospective designed sampling as a novel approach for Big Data challenges.
  • To foster discourse on integrating advanced design methods into Big Data analytics.

Main Methods:

  • Applying modern decision theoretic optimal experimental design principles.
  • Utilizing retrospective designed sampling strategies.
  • Examining a range of examples to demonstrate applicability and properties.

Main Results:

  • Retrospective designed sampling shows potential for improving Big Data analysis.
  • This approach offers advantageous inferential and computational properties.
  • The generality of this perspective across various Big Data problems is suggested.

Conclusions:

  • Optimal experimental design, particularly retrospective sampling, can enhance Big Data analysis.
  • Further research is needed on computational optimization for efficient retrospective designs.
  • Collaboration between optimization and experimental design communities is crucial for advancing Big Data analysis.