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Selection of the Blur Coefficient for Probability Density Kernel Estimates Under Conditions of Large Samples

  • A. V. LapkoEmail author
  • V. A. Lapko
GENERAL PROBLEMS OF METROLOGY AND MEASUREMENT TECHNIQUE
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A fast algorithm is proposed for choosing the blur factors of kernel functions of a non-parametric probability density estimate under conditions of large-scale statistical data. It is shown that the basis of the algorithm is the result of a study of the asymptotic properties of a new kernel probability density estimate. The properties of the developed algorithm are analyzed and the method of its application is formulated.

Keywords

kernel probability density estimate quick selection of blur factors discretization of the range of values of a random variable large volume statistical data 

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Copyright information

© 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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