Applied Mathematics Colloquium
Annenberg 105
Chi-Square and Classical Exact Tests Often Wildly Misreport Significance; the Remedy Lies in Computers
Rachel Ward,
Assistant Professor / Harrington Fellow,
Mathematics,
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.
http://arxiv.org/abs/1108.4126
Joint work with Will Perkins and Mark Tygert.
http://arxiv.org/abs/1108.4126
Joint work with Will Perkins and Mark Tygert.
For more information, please contact Sydney Garstang by phone at x4555 or by email at [email protected] or visit http://www.acm.caltech.edu.
Event Series
Applied Mathematics Colloquium Series