Numerical convergence of the block-maxima approach to the Generalized Extreme Value distribution

Mathematics – Dynamical Systems

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

34 pages, 7 figures; Journal of Statistical Physics 2011

Scientific paper

10.1007/s10955-011-0234-7

In this paper we perform an analytical and numerical study of Extreme Value distributions in discrete dynamical systems. In this setting, recent works have shown how to get a statistics of extremes in agreement with the classical Extreme Value Theory. We pursue these investigations by giving analytical expressions of Extreme Value distribution parameters for maps that have an absolutely continuous invariant measure. We compare these analytical results with numerical experiments in which we study the convergence to limiting distributions using the so called block-maxima approach, pointing out in which cases we obtain robust estimation of parameters. In regular maps for which mixing properties do not hold, we show that the fitting procedure to the classical Extreme Value Distribution fails, as expected. However, we obtain an empirical distribution that can be explained starting from a different observable function for which Nicolis et al. [2006] have found analytical results.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Numerical convergence of the block-maxima approach to the Generalized Extreme Value distribution 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 Numerical convergence of the block-maxima approach to the Generalized Extreme Value distribution, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Numerical convergence of the block-maxima approach to the Generalized Extreme Value distribution will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-321060

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.