@article{825379,
      recid = {825379},
      author = {Gramacki, Artur,},
      title = {Nonparametric kernel density estimation and its  computational aspects /},
      pages = {1 online resource.},
      abstract = {This book describes computational problems related to  kernel density estimation (KDE)? one of the most important  and widely used data smoothing techniques. A very detailed  description of novel FFT-based algorithms for both KDE  computations and bandwidth selection are presented. The  theory of KDE appears to have matured and is now well  developed and understood. However, there is not much  progress observed in terms of performance improvements.  This book is an attempt to remedy this. The book primarily  addresses researchers and advanced graduate or postgraduate  students who are interested in KDE and its computational  aspects. The book contains both some background and much  more sophisticated material, hence also more experienced  researchers in the KDE area may find it interesting. The  presented material is richly illustrated with many  numerical examples using both artificial and real datasets.  Also, a number of practical applications related to KDE are  presented.},
      url = {http://library.usi.edu/record/825379},
      doi = {https://doi.org/10.1007/978-3-319-71688-6},
}