Progress in Data-Based Bandwidth Selection for Kernel Density Estimation

@inproceedings{JonesProgressID,
  title={Progress in Data-Based Bandwidth Selection for Kernel Density Estimation},
  author={M. E. Jones and J. S. Marron and Simon J. Sheather}
}
We review the extensive recent literature on automatic, data-based selection of a global smoothing parameter in univariate kernel density estimation. Proposals are presented in a unified framework, making considerable reference to their theoretical properties as we go. The results of a major simulation study of the practical performance of many of these methods are summarised. Also, our remarks are further consolidated by describing a small portion of our practical experience on real datasets… CONTINUE READING
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