Dear Prof. King, I am conducting an analysis on the risk of aircraft crashes, comparing data from accidents and normal flights. Given the rare occurence of accidents, I have an extremely unblanced data set. A discussion on ALLSTAT (http://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind04&L=allstat&D=0&a… 7323) on this topic mentions your paper (Logistic Regression in Rare Events Data, 2001) as well as other methods to deal with rare events in logistic regression. One of them is to conduct logistic regression on subsets. Each subset has a 50:50 ratio of events to non-events, achieved by selecting all events and a random selection of the same number of non-events. The final parameter values would be defined by averaging the subsets' results. This method, apparently, is base on findings of Weiss & Provost (The Effect of Class Distribution on Classifier Learning: An Empirical Study, 2001), which deals with machine learning and data-mining methods. Given my lack of training in this field, I am unsure whether it is appropriate to treat logistic regression as a classifier/learner in the data-mining sense, such that Weiss & Provosts' conclusions also apply to the statistical technique. Your opinion on this matter would be greatly appreciated. Also, would it be possible to implement the corrections suggested in your Logistic Regression in Rare Events Data paper with SPSS/Excel? Thank you for your kind attention; I look forward to hearing from you soon. Yours sincerely, Derek Wong Research Assistant Transport Studies Group Civil & Building Engineering Dept. Loughborugh University Leics. LE11 3TU UK Tel.: +44 (0)1509 263171 ext. 4681 Fax: +44 (0)1509 223981 Project website: http://civil-unrest.lboro.ac.uk/cvaja/index.htm - relogit mailing list served by Harvard-MIT Data Center List Address: relogit(a)latte.harvard.edu Subscribe/Unsubscribe: http://lists.hmdc.harvard.edu/?info=relogit