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SUMMARY:Technology-driven statistics - Professor Terry Speed (Berkeley)
DTSTART:20080219T170000Z
DTEND:20080219T180000Z
UID:TALK8787@talks.cam.ac.uk
CONTACT:Helen Innes
DESCRIPTION:Once upon a time most statistical inference was carried out by
  regarding the data being analyzed as realizations of random variables who
 se joint distribution was determined up to some unknown parameters\, usual
 ly but not always finite-dimensional.  The challenge lay in dealing with t
 he unknowns when making appropriate inferences.  This view enabled us to d
 raw on a fine body mathematical theory\, which was comforting in that ther
 e seemed to be a solid foundation for what we were doing\, going back to K
 olmogorov's 1933 axiomatization of probability.  (Of course this is a gros
 s oversimplification\, and ignores major philosophical issues.)   In his f
 amous 1962 paper "The future of data analysis" Tukey questioned this ortho
 doxy\, and promoted data analysis\, a subject related to statistics\, but 
 one far less governed by mathematical theory\, and which did not appear to
  have any foundations.  Tukey's view is flourishing today\, yet mathematic
 al statistics lives\, and may itself be flourishing.  We now have many pro
 cesses - assays\, devices\, technologies - which can generate large amount
 s of data very quickly\, data for which a realistic joint distribution is 
 unimaginable\, no matter how we might parameterize.  By this I simply mean
  that we could never pass the statistician's Turing test - to simulate dat
 a indistinguishable from the real thing - with such data.  What do we do? 
  Well\, we combine statistics with data analysis (as perhaps we always hav
 e)\, doing things that seem appropriate\, alongside with things that would
  be correct\, given certain assumptions that are patently false.   I'll be
  illustrating these ideas with examples from biology\, more precisely\, hi
 gh-throughput biology.   It often seems to work\, sometimes rather well\, 
 and one day we may understand why.
LOCATION:Wolfson Room (MR 2) Centre for Mathematical Sciences\, Wilberforc
 e Road\, Cambridge
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