Indexing metadata

Infinite Divisibility of Gaussian Squares with Non-zero Means


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document Infinite Divisibility of Gaussian Squares with Non-zero Means
 
2. Creator Author's name, affiliation, country Michael B. Marcus; The City College of CUNY
 
2. Creator Author's name, affiliation, country Jay Rosen; The College of Staten Island of CUNY
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Gaussian vectors, infinite divisibility, Markov chains
 
3. Subject Subject classification 60G15, 60E07, 60J27
 
4. Description Abstract We give necessary and sufficient conditions for a Gaussian vector with non-zero mean, to have infinitely divisible squares for all scalar multiples of the mean, and show how the this vector is related to the local times of a Markov chain determined by the covariance matrix of the Gaussian vector. Our results add to results of Griffiths, Bapat, Eisenbaum and Kaspi.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s) NSF, PSCCUNY
 
7. Date (YYYY-MM-DD) 2008-06-27
 
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/1389
 
10. Identifier Digital Object Identifier 10.1214/ECP.v13-1389
 
11. Source Journal/conference title; vol., no. (year) Electronic Communications in Probability; Vol 13
 
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.