Robust computer-enabled tests of goodness-of-fit

Thursday, March 29, 2012 - 2:45pm - 3:30pm
Keller 3-180
Rachel Ward (The University of Texas at Austin)
If a discrete probability distribution in a model being tested for
goodness-of-fit is not close to uniform, forming the Pearson chi-square
statistic often involves renormalizing summands to different scales in order to uniformize the asymptotic distribution. This often leads to serious trouble in practice -- even in the absence of round-off errors -- as the talk will illustrate via numerous examples. Fortunately with the now widespread availability of computers, avoiding all the trouble is simple and easy: without
renormalization, the actual values taken by goodness-of-fit statistics are not humanly interpretable, but black-box computer programs can rapidly calculate their precise significance.

(joint work with Will Perkins and Mark Tygert)
MSC Code: