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Statistical methods and computing for big data.

Chun Wang1, Ming-Hui Chen1, Elizabeth Schifano1

  • 1215 Glenbrook Rd., Storrs, 06269, USA.

Statistics and Its Interface
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Computational statisticians tackle big data challenges using new methods like online updating for variable selection. Open-source R software packages help overcome memory and computing power limitations in big data analysis.

Keywords:
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Area of Science:

  • Statistics
  • Computational Statistics
  • Data Science

Background:

  • Big data presents significant challenges to traditional statistical analysis due to its massive volume, velocity, and complexity.
  • The crucial role of computational statisticians in extracting scientific insights from big data is often underestimated.
  • Existing analytical tools struggle to process and analyze datasets that exceed standard computational capacities.

Purpose of the Study:

  • To summarize recent statistical methodologies and software developments designed to address big data challenges.
  • To highlight the application of these methods in scientific discovery and data analysis.
  • To introduce an extension of the online updating approach for variable selection in stream data.

Main Methods:

  • Methodologies are categorized into subsampling-based, divide and conquer, and online updating for stream data.
  • The online updating approach is extended to include variable selection using established criteria.
  • Performance of these methods is evaluated through simulation studies using stream data.
  • Open-source R software packages are reviewed for their utility in handling large-scale data.

Main Results:

  • The study details statistical approaches for managing big data, including novel online updating techniques for variable selection.
  • Simulation results demonstrate the effectiveness of the extended online updating method in stream data analysis.
  • Open-source R packages are identified as key tools for overcoming computational barriers in big data analytics.
  • A case study on airline delay prediction using logistic regression illustrates the practical application of these tools.

Conclusions:

  • Recent advancements in statistical methodologies and open-source software provide effective solutions for big data analysis.
  • The online updating approach offers a promising avenue for variable selection in dynamic, high-velocity data streams.
  • Computational statisticians are vital for leveraging big data for scientific discovery, supported by powerful R packages.