A functional limit theorem for the profile of search trees
Abstract
We study the profile of random search trees including binary search trees and -ary search trees. Our main result is a functional limit theorem of the normalized profile for in a certain range of . A central feature of the proof is the use of the contraction method to prove convergence in distribution of certain random analytic functions in a complex domain. This is based on a general theorem concerning the contraction method for random variables in an infinite-dimensional Hilbert space. As part of the proof, we show that the Zolotarev metric is complete for a Hilbert space.
Cite
@article{arxiv.math/0609385,
title = {A functional limit theorem for the profile of search trees},
author = {Michael Drmota and Svante Janson and Ralph Neininger},
journal= {arXiv preprint arXiv:math/0609385},
year = {2008}
}
Comments
Published in at http://dx.doi.org/10.1214/07-AAP457 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)