Indexing metadata

Free Generalized Gamma Convolutions


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document Free Generalized Gamma Convolutions
 
2. Creator Author's name, affiliation, country Victor Perez Abreu; CIMAT
 
2. Creator Author's name, affiliation, country Noriyoshi Sakuma; Keio University
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Free probability; infinitely divisible distribution; generalized gamma convolutions; random matrices
 
3. Subject Subject classification 15A52; 46L54; 60E07
 
4. Description Abstract The so-called Bercovici-Pata bijection maps the set of classical infinitely divisible laws to the set of free infinitely divisible laws. The purpose of this work is to study the free infinitely divisible laws corresponding to the classical Generalized Gamma Convolutions (GGC). Characterizations of their free cumulant transforms are derived as well as free integral representations with respect to the free Gamma process. A random matrix model for free GGC is built consisting of matrix random integrals with respect to a classical matrix Gamma process. Nested subclasses of free GGC are shown to converge to the free stable class of distributions.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s) Japan Society for the Promotion of Science
 
7. Date (YYYY-MM-DD) 2008-10-14
 
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/1413
 
10. Identifier Digital Object Identifier 10.1214/ECP.v13-1413
 
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.