Dette, HolgerWeißbach, Rafael2008-11-262008-11-262008-11-26http://hdl.handle.net/2003/2587810.17877/DE290R-14464In 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: 62M02enBandwidth selectionHazard rateKernel estimationNearest neighbor bandwidthRule of thumbVariable bandwidth004report