A LRT framework for fast spatial anomaly detection

  title={A LRT framework for fast spatial anomaly detection},
  author={Mingxi Wu and Xiuyao Song and Chris Jermaine and Sanjay Ranka and John Gums},
Given a spatial data set placed on an n x n grid, our goal is to find the rectangular regions within which subsets of the data set exhibit anomalous behavior. We develop algorithms that, given any user-supplied arbitrary likelihood function, conduct a likelihood ratio hypothesis test (LRT) over each rectangular region in the grid, rank all of the rectangles based on the computed LRT statistics, and return the top few most interesting rectangles. To speed this process, we develop methods to… CONTINUE READING
Highly Cited
This paper has 22 citations. REVIEW CITATIONS


Publications referenced by this paper.
Showing 1-2 of 2 references

Spatial scan statistics: model, calculat ions, and applications

  • M. Kulldorff
  • InScan Statistics and Applications ,
  • 1999
Highly Influential
11 Excerpts

Similar Papers

Loading similar papers…