# Kernel Density Estimation Bias under Minimal Assumptions

@article{Skorski2019KernelDE, title={Kernel Density Estimation Bias under Minimal Assumptions}, author={Maciej Skorski}, journal={ArXiv}, year={2019}, volume={abs/1901.00331} }

Kernel Density Estimation is a very popular technique of approximating a density function from samples. The accuracy is generally well-understood and depends, roughly speaking, on the kernel decay and local smoothness of the true density. However concrete statements in the literature are often invoked in very specific settings (simplified or overly conservative assumptions) or miss important but subtle points (e.g. it is common to heuristically apply Taylor's expansion globally without…

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