Borrowing strength: a likelihood ratio test for related sparse signals | Zendy

Ernst C. Wit | Zendy; David J. Bakewell | Zendy

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Borrowing strength: a likelihood ratio test for related sparse signals

Author(s) -

Ernst C. Wit,

David J. Bakewell

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts316

Subject(s) - statistic , computer science , variance (accounting) , field (mathematics) , statistics , software , monte carlo method , source code , likelihood ratio test , code (set theory) , algorithm , data mining , mathematics , set (abstract data type) , accounting , pure mathematics , business , programming language , operating system

Cancer biology is a field where the complexity of the phenomena battles against the availability of data. Often only a few observations per signal source, i.e. genes, are available. Such scenarios are becoming increasingly more relevant as modern sensing technologies generally have no trouble in measuring lots of channels, but where the number of subjects, such as patients or samples, is limited. In statistics, this problem falls under the heading 'large p, small n'. Moreover, in such situations the use of asymptotic analytical results should generally be mistrusted.

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