000004884 001__ 4884
000004884 005__ 20141119144601.0
000004884 04107 $$aeng
000004884 046__ $$k2002-06-02
000004884 100__ $$aLiu, H.
000004884 24500 $$aMODEL ERRORS: ANALYSIS BY CLASSIFICATION TREES AND IMPLICATIONS ON OPTIMAL DESIGN

000004884 24630 $$n15.$$pProceedings of the 15th ASCE Engineering Mechanics Division Conference
000004884 260__ $$bColumbia University in the City of New York
000004884 506__ $$arestricted
000004884 520__ $$2eng$$aIn the search for the optimal design of a system, approximate models are often needed. Before such models are used, a regression is usually performed to fit the model to a more accurate, reference model. Inherent in such a fitting procedure is a statistical model for the errors. In this extended abstract, the model errors is examined for potential use in the optimal design problem. With the randomness in the errors, a probabilistic view is still needed, however, the approach developed herein is fundamentally different from that used in statistical regression. With the focus on design, it is shown that the outliers, which are ignored or otherwise discounted in statistical regression, can be used to identify system features that may potentially lead to improved design. 

000004884 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000004884 653__ $$aoptimal design, model error, Bayesian analysis, classification tree.

000004884 7112_ $$a15th ASCE Engineering Mechanics Division Conference$$cNew York (US)$$d2002-06-02 / 2002-06-05$$gEM2002
000004884 720__ $$aLiu, H.$$iIgusa, T.
000004884 8560_ $$ffischerc@itam.cas.cz
000004884 8564_ $$s258149$$uhttp://invenio.itam.cas.cz/record/4884/files/488.pdf$$yOriginal version of the author's contribution as presented on CD, .
000004884 962__ $$r4594
000004884 980__ $$aPAPER