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SUMMARY:Simultaneous break point detection and variable selection in quant
 ile regression models - Aue\, A (UC Davis)
DTSTART:20140116T103000Z
DTEND:20140116T110000Z
UID:TALK49981@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:This talk discusses new model fitting techniques for quantiles
  of an observed data sequence\, including methods for data segmentation an
 d variable selection. The main contribution\, however\, is in providing a 
 means to perform these two tasks simultaneously. This is achieved by match
 ing the data with the best-fitting piecewise quantile regression model\, w
 here the fit is determined by a penalization derived from the minimum desc
 ription length principle. The resulting optimization problem is solved wit
 h the use of genetic algorithms. The proposed\, fully automatic procedures
  are\, unlike traditional break point procedures\, not based on repeated h
 ypothesis tests\, and do not require\, unlike most variable selection proc
 edures\, the specification of a tuning parameter. Theoretical large-sample
  properties are derived. Empirical comparisons with existing break point a
 nd variable selection methods for quantiles indicate that the new procedur
 es work well in practice.\n
LOCATION:Seminar Room 1\, Newton Institute
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