A mixture model for unsupervised tail estimation

Details A mixture model for unsupervised tail estimation A mixture model for unsupervised tail estimation

2009-02-24

arxiv.org/abs/0902.4137v1

Statistics

Methodology

Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics

Scientific paper

This paper proposes a new method to combine several densities such that each density dominates a separate part of a joint distribution. The method is fully unsupervised, i.e. the parameters in the densities and the thresholds are simultaneously estimated. The approach uses cdf functions in the mixing. This makes it easy to estimate parameters and the resulting density is smooth. Our method may be used both when the tails are heavier and lighter than the rest of the distribution. The presented model is compared with other published models and a very simple model using a univariate transformation.

Affiliated with

Also associated with

No associations

Rating

A mixture model for unsupervised tail estimation 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 A mixture model for unsupervised tail estimation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A mixture model for unsupervised tail estimation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-172354