Attention conservation notice: Only of interest if you (1) care about statistical inference with network data, and (2) will be in Pittsburgh next week.
A (perhaps) too-skeptical view of statistics is that we should always think we have $ n=1 $, because our data set is a single, effectively irreproducible, object. With a lot of care and trouble, we can obtain things very close to independent samples in surveys and experiments. When we get to time series or spatial data, independence becomes a myth we must abandon, but we still hope that we can break up the data set into many nearly-independent chunks. To make those ideas plausible, though, we need to have observations which are widely separated from each other. And those asymptotic-independence stories themselves seem like myths when we come to networks, where, famously, everyone is close to everyone else. The skeptic would, at this point, refrain from drawing any inference whatsoever from network data. Fortunately for the discipline, Betsy Ogburn is not such a skeptic.
As always, the talk is free and open to the public.
Posted at November 09, 2015 22:14 | permanent link