Elizabeth Meckes

Case Western Reserve

Quantitative asymptotics of graphical projection pursuit.

There is a large class of results which say that, for parametrized collection of random variables, a random variable from the collection behaves a certain way for "most" values of the parameter. A nice example of such a result is a theorem of Persi Diaconis and David Freedman, which roughly says that if you have a large collection of high-dimensional data points, most one-dimensional projections of the data will look Gaussian even if the data have no particular structure. In this talk, I'll discuss a quantitative version of this result and its proof, which uses Stein's method, the concentration of measure phenomenon, and the geometry of certain function spaces.

File translated from T_EX by T_TH, version 3.85.
On 12 Jan 2009, 16:33.