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Estimation of conditional distribution functions from data with additional errors applied to shape optimization

Hansmann, Matthias ; Horn, Benjamin M. ; Kohler, Michael ; Ulbrich, Stefan (2022)
Estimation of conditional distribution functions from data with additional errors applied to shape optimization.
In: Metrika, 85 (3)
doi: 10.1007/s00184-021-00831-4
Artikel, Bibliographie

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Kurzbeschreibung (Abstract)

We study the problem of estimating conditional distribution functions from data containing additional errors. The only assumption on these errors is that a weighted sum of the absolute errors tends to zero with probability one for sample size tending to infinity. We prove sufficient conditions on the weights (e.g. fulfilled by kernel weights) of a local averaging estimate of the codf, based on data with errors, which ensure strong pointwise consistency. We show that two of the three sufficient conditions on the weights and a weaker version of the third one are also necessary for the spc. We also give sufficient conditions on the weights, which ensure a certain rate of convergence. As an application we estimate the codf of the number of cycles until failure based on data from experimental fatigue tests and use it as objective function in a shape optimization of a component.

Typ des Eintrags: Artikel
Erschienen: 2022
Autor(en): Hansmann, Matthias ; Horn, Benjamin M. ; Kohler, Michael ; Ulbrich, Stefan
Art des Eintrags: Bibliographie
Titel: Estimation of conditional distribution functions from data with additional errors applied to shape optimization
Sprache: Englisch
Publikationsjahr: April 2022
Ort: Berlin ; Heidelberg
Verlag: Springer
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Metrika
Jahrgang/Volume einer Zeitschrift: 85
(Heft-)Nummer: 3
DOI: 10.1007/s00184-021-00831-4
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Kurzbeschreibung (Abstract):

We study the problem of estimating conditional distribution functions from data containing additional errors. The only assumption on these errors is that a weighted sum of the absolute errors tends to zero with probability one for sample size tending to infinity. We prove sufficient conditions on the weights (e.g. fulfilled by kernel weights) of a local averaging estimate of the codf, based on data with errors, which ensure strong pointwise consistency. We show that two of the three sufficient conditions on the weights and a weaker version of the third one are also necessary for the spc. We also give sufficient conditions on the weights, which ensure a certain rate of convergence. As an application we estimate the codf of the number of cycles until failure based on data from experimental fatigue tests and use it as objective function in a shape optimization of a component.

Freie Schlagworte: Conditional distribution function estimation, Consistency, Experimental fatigue tests, Local averaging estimate, Shape optimization, Isogeometric analysis
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Mathematics Subject Classification: 62G05, 62G20

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 510 Mathematik
Fachbereich(e)/-gebiet(e): 04 Fachbereich Mathematik
04 Fachbereich Mathematik > Optimierung
04 Fachbereich Mathematik > Stochastik
Hinterlegungsdatum: 19 Mär 2024 10:10
Letzte Änderung: 19 Mär 2024 10:10
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