Asymptotic Geometry of Multiple Hypothesis Testing

Authors:
M. B. Westover
Affiliations:
Dept. of Neurology, Massachusetts Gen. Hosp., Boston, MA
Venue:
IEEE Transactions on Information Theory
Year:
2008

Citing 0
Cited 2

Review of "Methods of information geometry" by Shunichi Amari and Hiroshi Nagaoka, American Mathematical Society and Oxford University Press, 2000

IEEE Transactions on Information Theory
Multiple objects: error exponents in hypotheses testing and identification

Information Theory, Combinatorics, and Search Theory

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Abstract

We present a simple geometrical interpretation for the solution to the multiple hypothesis testing problem in the asymptotic limit. Under this interpretation, the optimal decision rule is a nearest neighbor classifier on the probability simplex.