Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286790 
Year of Publication: 
2021
Citation: 
[Journal:] Metrika [ISSN:] 1435-926X [Volume:] 85 [Issue:] 3 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 323-343
Publisher: 
Springer, Berlin, Heidelberg
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.
Subjects: 
Conditional distribution function estimation
Consistency
Experimental fatigue tests
Local averaging estimate
Shape optimization
Isogeometric analysis
JEL: 
G05
G20
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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