Indexing metadata

Weak approximation of the fractional Brownian sheet from random walks


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document Weak approximation of the fractional Brownian sheet from random walks
 
2. Creator Author's name, affiliation, country Zhi Wang; Donghua University; China
 
2. Creator Author's name, affiliation, country Litan Yan; Donghua University; China
 
2. Creator Author's name, affiliation, country Xianye Yu; Donghua University; China
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Fractional Brownian sheet; random walks; stochastic heat equation; weak convergence
 
3. Subject Subject classification 60B10; 60G15; 60H15
 
4. Description Abstract In this paper, we show an approximation in law of the fractional Brownian sheet by random walks. As an application, we consider a quasilinear stochastic heat equation with Dirichlet boundary conditions driven by an additive fractional noise.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2013-12-01
 
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/2878
 
10. Identifier Digital Object Identifier 10.1214/ECP.v18-2878
 
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.