Heavy-Tailed Processes for Selective Shrinkage

Details Heavy-Tailed Processes for Selective Shrinkage Heavy-Tailed Processes for Selective Shrinkage

2010-06-19

arxiv.org/abs/1006.3901v2

Statistics

Machine Learning

10 pages, 4 figures, submitted for publication

Scientific paper

Heavy-tailed distributions are frequently used to enhance the robustness of regression and classification methods to outliers in output space. Often, however, we are confronted with "outliers" in input space, which are isolated observations in sparsely populated regions. We show that heavy-tailed stochastic processes (which we construct from Gaussian processes via a copula), can be used to improve robustness of regression and classification estimators to such outliers by selectively shrinking them more strongly in sparse regions than in dense regions. We carry out a theoretical analysis to show that selective shrinkage occurs, provided the marginals of the heavy-tailed process have sufficiently heavy tails. The analysis is complemented by experiments on biological data which indicate significant improvements of estimates in sparse regions while producing competitive results in dense regions.

Affiliated with

Also associated with

No associations

Rating

Heavy-Tailed Processes for Selective Shrinkage does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Heavy-Tailed Processes for Selective Shrinkage, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Heavy-Tailed Processes for Selective Shrinkage will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-185886