Adelaide Research & Scholarship
Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/17833
| Citations | ||
| Scopus | Web of Science® | Altmetric |
|---|---|---|
|
?
|
?
|
|
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Torokhti, A. | - |
| dc.contributor.author | Howlett, P. | - |
| dc.contributor.author | Pearce, C. | - |
| dc.date.issued | 2005 | - |
| dc.identifier.citation | Annals of Operations Research, 2005; 133(1-3):285-302 | - |
| dc.identifier.issn | 0254-5330 | - |
| dc.identifier.issn | 1572-9338 | - |
| dc.identifier.uri | http://hdl.handle.net/2440/17833 | - |
| dc.description | The original publication is available at www.springerlink.com | - |
| dc.description.abstract | We present a new approach to the optimal estimation of random vectors. The approach is based on a combination of a specific iterative procedure and the solution of a best approximation problem with a polynomial approximant. We show that the combination of these new techniques allow us to build a computationally effective and flexible estimator. The strict justification of the proposed technique is provided. | - |
| dc.description.statementofresponsibility | Anatoli Torokhti, Phil Howlett and Charles Pearce | - |
| dc.language.iso | en | - |
| dc.publisher | Kluwer Academic Publishers | - |
| dc.source.uri | http://www.springerlink.com/content/gg5p715557g16342/ | - |
| dc.subject | error minimization | - |
| dc.subject | stochastic vector | - |
| dc.subject | optimal estimate | - |
| dc.title | Optimal recursive estimation of raw data | - |
| dc.type | Journal article | - |
| dc.identifier.doi | 10.1007/s10479-004-5039-5 | - |
| pubs.publication-status | Published | - |
| Appears in Collections: | Applied Mathematics publications Aurora harvest 2 | |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.