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Asymptotic Statistics A. W. van der Vaart
Publisher: Cambridge University Press

The concept of Local Asymptotic Normality (LAN) – introduced by Lucien LeCam – is one of the most important and fundamental ideas of the general asymptotic statistical theory. Getis and Ord's G and Moran's I statistics, as well as their local versions Gi and Ii, have been widely used in spatial data analysis. Dear statistics-experts, I have a comprehensive question concerning the Asimov dataset used in the asymptotic formulae (Eur. The issue that brought it up was that sometimes statisticians like to work on asymptotic results. Isn't it ironic that almost all known results in asymptotic statistics don't scale well with data? Prior research has shown that the G statistic is asymptotically normal under weak regularity conditions. If you have very large samples an asymptotic approach (using Wald z or chi-square statistics) is probably just fine. Attention conservation notice: Only interesting if you (1) care about dividing networks into The talk discusses a number of methods and models for community detection under a general asymptotic framework which allows us to evaluate and compare methods in terms of consistency. This, by itself, isn't The program has also hired people with non-statistics PhDs, like sociology and economics. There are several things The results of asymptotic statistics scale beautifully when the model is correct. "Community Detection and Link Prediction in Networks" (Next Week at the Statistics Seminar). Actually I've been meaning to mention a nice bit of statistical theory called Extreme Value Theory on here for some time, because not so many people seem to be aware of it, but somehow I never got around to writing about it.