Szczegóły publikacji
Opis bibliograficzny
Optimal conditional estimation: average case setting / B. KACEWICZ // Journal of Optimization Theory and Applications ; ISSN 0022-3239. — 2001 — vol. 109 no. 3, s. 647–664. — Bibliogr. s. 663–664, Abstr.
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 9536 |
|---|---|
| Data dodania do BaDAP | 2002-07-15 |
| DOI | 10.1023/A:1017524023577 |
| Rok publikacji | 2001 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Journal of Optimization Theory and Applications |
Abstract
We consider an estimation problem which appears in the identification of systems by means of restricted complexity models: find the optimal approximation to an element of a linear normed space (a system) based on noisy information, subject to the restriction that approximations (models) can be selected from a prescribed subspace;M of the problem element space. In contrast to the worst-case optimization criterion, which may be pessimistic, in this paper the quality of an identification algorithm is measured by its local average performance. Two types of local average errors are considered: for a given information (measurement) y and for a given unknown element x, the latter in two versions. For a wide spectrum of norms in the measurement space, we define an optimal algorithm and give expressions for its average errors which show the dependence on information, information errors, unmodelled dynamics, and norm in the measurement space.
