Indexing metadata

Müntz linear transforms of Brownian motion


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document Müntz linear transforms of Brownian motion
 
2. Creator Author's name, affiliation, country Larbi Alili; University of Warwick; United Kingdom
 
2. Creator Author's name, affiliation, country Ching-Tang Wu; National Taitung University; Taiwan, Province of China
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Enlargement of filtration ; Gaussian process ; M\"untz polynomials ; noncanonical representation ; self-reproducing kernel ; Volterra representation
 
3. Subject Subject classification 45D05 ; 60G15
 
4. Description Abstract We consider a class of Volterra linear transforms of Brownian motion associated to a sequence of Müntz Gaussian spaces and determine explicitly their kernels; the kernels take a simple form when expressed in terms of Müntz-Legendre polynomials. These are new explicit examples of progressive Gaussian enlargement of a Brownian filtration. We give a necessary and sufficient condition for the existence of kernels of infinite order associated to an infinite dimensional Müntz Gaussian space; we also examine when the transformed Brownian motion remains a semimartingale in the filtration of the original process. This completes some already obtained partial answers to the aforementioned problems in the infinite dimensional case.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s) ANR-09-Blan-0084-01
 
7. Date (YYYY-MM-DD) 2014-03-22
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier http://ejp.ejpecp.org/article/view/2424
 
10. Identifier Digital Object Identifier 10.1214/EJP.v19-2424
 
11. Source Journal/conference title; vol., no. (year) Electronic Journal of Probability; Vol 19
 
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.