|Title:||Bias in nearest-neighbor hazard estimation|
|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 crossvalidation, and a plug-in selector. A Monte Carlo simulation within the threeparameter exponentiated Weibull distribution indicates that a counterfactual 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. Crossvalidation has a similar bias to the rule of thumb, but also with high variability. AMS: 62M02|
|Subject Headings:||Bandwidth selection|
Nearest neighbor bandwidth
Rule of thumb
|Appears in Collections:||Sonderforschungsbereich (SFB) 475|
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