ECP Reading Tools

Exponential tail bounds for max-recursive sequences

Dublin Core		PKP Metadata Items	Metadata for this Document
1.	Title	Title of document	Exponential tail bounds for max-recursive sequences
2.	Creator	Author's name, affiliation, country	Ludger Rueschendorf; Mathematical Stochastics
2.	Creator	Author's name, affiliation, country	Eva-Maria Schopp; Mathematical Stochastics
3.	Subject	Discipline(s)
3.	Subject	Keyword(s)	recursive algorithm, exponential bounds, divide and conquer algorithm, probabilistic analysis of algorithms
3.	Subject	Subject classification	60705, 68Q25, 68W40
4.	Description	Abstract	Exponential tail bounds are derived for solutions of max-recursive equations and for max-recursive random sequences, which typically arise as functionals of recursive structures, of random trees or in recursive algorithms. In particular they arise in the worst case analysis of divide and conquer algorithms, in parallel search algorithms or in the height of random tree models. For the proof we determine asymptotic bounds for the moments or for the Laplace transforms and apply a characterization of exponential tail bounds due to Kasahara (1978).
5.	Publisher	Organizing agency, location
6.	Contributor	Sponsor(s)
7.	Date	(YYYY-MM-DD)	2006-11-11
8.	Type	Status & genre	Peer-reviewed Article
8.	Type	Type
9.	Format	File format	PDF
10.	Identifier	Uniform Resource Identifier	http://ecp.ejpecp.org/article/view/1227
10.	Identifier	Digital Object Identifier	10.1214/ECP.v11-1227
11.	Source	Journal/conference title; vol., no. (year)	Electronic Communications in Probability; Vol 11
12.	Language	English=en
14.	Coverage	Geo-spatial location, chronological period, research sample (gender, age, etc.)
15.	Rights	Copyright and permissions	The Electronic Journal of Probability applies the Creative Commons Attribution License (CCAL) to all articles we publish in this journal. Under the CCAL, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles published in EJP, so long as the original authors and source are credited. This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available. Summary of the Creative Commons Attribution License You are free to copy, distribute, display, and perform the work to make derivative works to make commercial use of the work under the following condition of Attribution: others must attribute the work if displayed on the web or stored in any electronic archive by making a link back to the website of EJP via its Digital Object Identifier (DOI), or if published in other media by acknowledging prior publication in this Journal with a precise citation including the DOI. For any further reuse or distribution, the same terms apply. Any of these conditions can be waived by permission of the Corresponding Author.