Fast Algorithm for Choosing Blur Coefficients in Multidimensional Kernel Probability Density Estimates

  • A. V. LapkoEmail author
  • V. A. Lapko

A method is proposed for quickly choosing the blur coefficients of kernel functions in a non-parametric estimate of a multidimensional probability density of Rosenblatt–Parzen type. The technique is based on the analysis of the asymptotic properties of a multidimensional probability density estimate. The properties of the fast algorithm for choosing the blur coefficients of a kernel probability density estimate are investigated.


non-parametric estimation of multidimensional probability density choice of blur coefficients Rosenblatt–Parzen estimate fast optimization algorithm asymptotic properties multidimensional data analysis 


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Computational ModelingSiberian Branch of the Russian Academy of SciencesKrasnoyarskRussia
  2. 2.Reshetnev Siberian State University of Science and TechnologyKrasnoyarskRussia

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