"Dependence Estimation in High-Dimensional Euclidean Spaces" (Next Week at the Statistics Seminar)
For the first seminar of the new academic year, we are very pleased to welcome —
- Barnabas Pcozos, "Dependence Estimation in High-Dimensional Euclidean Spaces"
- Abstract: In this presentation we review some recent results on
dependence estimation in high-dimensional Euclidean spaces. We survey several
different dependence measures with their estimators and discuss the main
difficulties and open problems with a special emphasis on how to avoid the
curse of dimensionality. We will also propose a new dependence measure which
extends the maximum mean discrepancy to the copula of the joint distribution.
We prove that this approach has several advantageous properties. Similarly to
Shannon's mutual information, the proposed dependence measure is invariant to
any strictly increasing transformation of the marginal variables. This is
important in many appications, for example, in feature selection. The
estimator is consistent, robust to outliers, and does not suffer from the curse
of dimensionality. We derive upper bounds on the convergence rate and propose
independence tests too. We illustrate the theoretical contributions through a
series of numerical experiments.
- Time and place: 4--5 pm on Monday, 10 September 2012, in the
Adamson Wing (136) of Baker Hall
As always, the seminar is free and open to the public.
Enigmas of Chance
Posted at September 06, 2012 14:49 | permanent link