Indexing metadata

On the Innovations Conjecture of Nonlinear Filtering with Dependent Data


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document On the Innovations Conjecture of Nonlinear Filtering with Dependent Data
 
2. Creator Author's name, affiliation, country Andrew Heunis; University of Waterloo
 
2. Creator Author's name, affiliation, country Vladimir Lucic; Barclays Capital
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) nonlinear filter; innovations conjecture; pathwise-uniqueness
 
3. Subject Subject classification 60G35
 
4. Description Abstract We establish the innovations conjecture for a nonlinear filtering problem in which the signal to be estimated is conditioned by the observations. The approach uses only elementary stochastic analysis, together with a variant due to J.M.C. Clark of a theorem of Yamada and Watanabe on pathwise-uniqueness and strong solutions of stochastic differential equations.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s) NSERC of Canada
 
7. Date (YYYY-MM-DD) 2008-11-05
 
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/585
 
10. Identifier Digital Object Identifier 10.1214/EJP.v13-585
 
11. Source Journal/conference title; vol., no. (year) Electronic Journal of 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.