Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction

@article{Madigan2002LikelihoodBasedDS,
  title={Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction},
  author={David Madigan and Nandini Raghavan and William DuMouchel and Martha Nason and Christian Posse and Greg Ridgeway},
  journal={Data Mining and Knowledge Discovery},
  year={2002},
  volume={6},
  pages={173-190}
}
Squashing is a lossy data compression technique that preserves statistical information. Specifically, squashing compresses a massive dataset to a much smaller one so that outputs from statistical analyses carried out on the smaller (squashed) dataset reproduce outputs from the same statistical analyses carried out on the original dataset. Likelihood-based data squashing (LDS) differs from a previously published squashing algorithm insofar as it uses a statistical model to squash the data. The… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 16 references

Squashing at le atter

  • W. DuMouchel, C. Volinsky, T. Johnson, C. Cortes, D. Pregibon
  • Proceedings of the Fifth ACM Conference on…
  • 1999
1 Excerpt

Report of the working group on storage I / O issues in large - scale computing

  • G. A. Gibson, J. S. Vitter, J. Wilkes
  • ACM Computing Surveys
  • 1996
1 Excerpt

Empirical Model Building and Response Surfaces

  • G.E.P. Box, N. R. Draper
  • John Wiley & Sons, New York, NY, USA, Bradley, P…
  • 1987
1 Excerpt

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