Speaker
Chad Shafer
(CMU)
Description
These two lectures will provide an overview of the utility and challenges of
nonparametric approaches to statistical inference. Nonparametric inference
has great promise to "let data speak for themselves" and enable a researcher
to avoid restrictive model assumptions. This can be especially important
when massive sets of data need to be summarized in a way that preserved
scientific information while not missing important features. Topics covered
will include nonparametric density estimation and nonparametric regression.
Focus will be placed on the curse of dimensionality, and the crucial role
it plays in motivating particular nonparametric approaches. Underlying
statistical theory will be presented.
