## NON-STANDARD PROBLEMS AND MASSIVE DATA SETS

Moulinath Banerjee

University of Michigan, Ann Arbor

We discuss two methods of making inference on parameters in
non-standard problems where the
estimates under consideration are asymptotically non-Gaussian.
The first relies on zoom-in strategies that
sample in stages from a large data-base. These allow precise
estimates to be constructed by sampling a very
small fraction of the available data. The second uses sample
splitting in non-standard problems which, in addition
to computational efficacy, also often buys precision over an
analysis of the entirety of data at one stroke.