Open Access
March 2009 Efficient blind search: Optimal power of detection under computational cost constraints
Nicolai Meinshausen, Peter Bickel, John Rice
Ann. Appl. Stat. 3(1): 38-60 (March 2009). DOI: 10.1214/08-AOAS180


Some astronomy projects require a blind search through a vast number of hypotheses to detect objects of interest. The number of hypotheses to test can be in the billions. A naive blind search over every single hypothesis would be far too costly computationally. We propose a hierarchical scheme for blind search, using various “resolution” levels. At lower resolution levels, “regions” of interest in the search space are singled out with a low computational cost. These regions are refined at intermediate resolution levels and only the most promising candidates are finally tested at the original fine resolution. The optimal search strategy is found by dynamic programming. We demonstrate the procedure for pulsar search from satellite gamma-ray observations and show that the power of the naive blind search can almost be matched with the hierarchical scheme while reducing the computational burden by more than three orders of magnitude.


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Nicolai Meinshausen. Peter Bickel. John Rice. "Efficient blind search: Optimal power of detection under computational cost constraints." Ann. Appl. Stat. 3 (1) 38 - 60, March 2009.


Published: March 2009
First available in Project Euclid: 16 April 2009

zbMATH: 1161.62087
MathSciNet: MR2668699
Digital Object Identifier: 10.1214/08-AOAS180

Keywords: dynamic programming , group testing , multiple testing , pulsar detection

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.3 • No. 1 • March 2009
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