Indexing metadata

Compound Poisson approximation with association or negative association via Stein's method


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document Compound Poisson approximation with association or negative association via Stein's method
 
2. Creator Author's name, affiliation, country Fraser Daly; University of Bristol; United Kingdom
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Compound Poisson approximation; Stein's method; (negative) association; runs; urn model with overflow; extremes
 
3. Subject Subject classification 62E17
 
4. Description Abstract In this note we show how the properties of association and negative association can be combined with Stein's method for compound Poisson approximation.  Applications include $k$--runs in iid Bernoulli trials, an urn model with urns of limited capacity and extremes of random variables.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s) Swiss SNF; University of Zurich
 
7. Date (YYYY-MM-DD) 2013-04-20
 
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/2751
 
10. Identifier Digital Object Identifier 10.1214/ECP.v18-2751
 
11. Source Journal/conference title; vol., no. (year) Electronic Communications in Probability; Vol 18
 
12. Language English=en 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.