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Challenges of Big Data Analysis.

Jianqing Fan1, Fang Han2, Han Liu3

  • 1Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA; jqfan@princeton.edu .

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Summary
This summary is machine-generated.

Big Data offers new insights but presents computational and statistical challenges. New methods are needed to address issues like scalability and spurious correlations for accurate Big Data analysis.

Keywords:
Big Datadata storagehigh dimensional dataincidental endogeneitylarge-scale optimizationmassive datamassively parallel data processingnoise accumulationrandom projectionscalabilityspurious correlation

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Area of Science:

  • Data Science
  • Computational Statistics
  • Big Data Analytics

Background:

  • Big Data enables discovery of subtle population patterns and heterogeneities.
  • Massive sample size and high dimensionality pose computational and statistical challenges.
  • Existing methods struggle with scalability, storage, noise, spurious correlations, and endogeneity.

Purpose of the Study:

  • To overview Big Data features and their impact on statistical and computational paradigms.
  • To explore new perspectives on Big Data analysis and computation.
  • To highlight the limitations of current statistical methods due to incidental endogeneity.

Main Methods:

  • Overview of Big Data characteristics.
  • Analysis of paradigm shifts in statistical and computational methods.
  • Discussion of computing architectures for Big Data.
  • Examination of incidental endogeneity in Big Data inference.

Main Results:

  • Big Data necessitates new computational and statistical paradigms.
  • Incidental endogeneity invalidates exogenous assumptions in many Big Data methods.
  • Current approaches risk wrong statistical inferences and scientific conclusions.

Conclusions:

  • A paradigm shift in statistical and computational methods is crucial for Big Data.
  • Addressing incidental endogeneity is key for valid Big Data analysis.
  • New computing architectures and methods are required to harness Big Data's potential.