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...te Speakers: Leo Breiman, University of California, Berkeley Jerome Friedman, Stanford University Richard Olshen, Stanford University Charles Stone, University of California, Berkeley Conference Sponsor: Salford Systems The conferences are intended to serve several functions: o A festschrift and opportunity to honor the four authors of CART and meet with them in person. Each is planning to offer a keynote paper. o A venue to exchange ideas and experiences focused on the practice of data mining. o A networking opportunity leading to the creation of local user groups and the est...

Hello all, I believe this can be done using bootstrap, but I am wondering if there is some other way that might be used to tackle this. #Let's say I have two pairs of samples: set.seed(100) s1 <- rnorm(100) s2 <- s1 + rnorm(100) x1 <- s1[1:99] y1 <- s2[1:99] x2 <- x1 y2 <- s2[2:100] #And both yield the following two correlations: cor(x1,y1) # 0.7568969 (cor1) cor(x2,y2)

...niversity of California, Berkeley Jerome Friedman, Stanford University Richard Olshen, Stanford University Charles Stone, University of California, Berkeley Conference Sponsor: Salford Systems, http://www.salford-systems.com The conferences are intended to serve several functions: o A festschrift and opportunity to honor the four authors of CART and meet with them in person. Each is planning to offer a keynote paper. o A venue to exchange ideas and experiences focused on the practice of data mining. o A networking opportunity leading to the creation of local user groups and the establish...

Here's a bug one of my students noticed. When you call plot() on a glm object, plot.lm gets called. The second plot it shows is supposed to give a normal QQ plot of the standard deviance residuals, but it doesn't. The glm object created by glm.fit returns something (the IRLS weights?) in fit$weights which plot.lm takes as observation weights, so you get strange residuals in the QQ