Create powerful statistical techniques to detect signals
A Case Western Reserve University research team from physics and statistics has recently created innovative statistical techniques that improve the chances of detecting a signal in large data sets. The new techniques can not only search for the "needle in the haystack" in particle physics, but also have applications in discovering a new galaxy, monitoring transactions for fraud and security risk, identifying the carrier of a virulent disease among millions of people or detecting cancerous tissues in a mammogram.
Case faculty members Ramani Pilla and Catherine Loader from statistics and Cyrus Taylor from physics report their findings in the article, "A New Technique for Finding Needles in Haystacks: A Geometric Approach to Distinguishing between a New Source and Random Fluctuations," December 2, in the journal, Physical Review Letters.
"As haystacks of information grow ever larger--and the needles ever smaller--the search for a signal becomes increasingly difficult to find using traditional approaches. There is a need for sophisticated new statistical methods," the researchers report.
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