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Standard kernel density estimation is subject to bias that can mask structure by flattening peaks and filling in troughs in the density. A number of methods of bias reduction have been proposed including approaches based on reweighting the contributions from the individual data points. We explore the potential of bias reduction by reweighting, and propose a new type of reweighted kernel density estimator...
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