Measurement Techniques

, Volume 61, Issue 6, pp 540–545 | Cite as

Fast Algorithm for Choosing Kernel Function Blur Coefficients in a Nonparametric Probability Density Estimate

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

A fast algorithm for choosing the blurring coefficients of kernel functions for a nonparametric probability density estimate is proposed, and its properties are investigated. The technique of interval estimation of the standard deviation of the nonparametric statistics under consideration is considered.


nonparametric estimation of probability density choice of blurring coefficients estimation of mean square deviation of probability density estimation 


This study was supported by the Russian Foundation for Basic Research (Grant No. 18-01-00251).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

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

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