Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/36586
Authors: 
Weißbach, Rafael
Dette, Holger
Year of Publication: 
2008
Series/Report no.: 
Technical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 2008,15
Abstract: 
In nonparametric curve estimation, the smoothing parameter is critical for performance. In order to estimate the hazard rate, we compare nearest neighbor selectors that minimize the quadratic, the Kullback-Leibler, and the uniform loss. These measures result in a rule of thumb, a cross-validation, and a plug-in selector. A Monte Carlo simulation within the three-parameter exponentiated Weibull distribution indicates that a counter-factual normal distribution, as an input to the selector, does provide a good rule of thumb. If bias is the main concern, minimizing the uniform loss yields the best results, but at the cost of very high variability. Cross-validation has a similar bias to the rule of thumb, but also with high variability.
Subjects: 
hazard rate
kernel smoothing
bandwidth selection
nearest neighbor bandwidth
rule of thumb
plug-in
cross-validation
credit risk
Document Type: 
Working Paper

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