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

  • GENERAL PROBLEMS OF METROLOGY AND MEASUREMENT TECHNIQUE
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Measurement Techniques Aims and scope

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.

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Authors and Affiliations

  1. Institute of Computational Modeling, Siberian Branch of the Russian Academy of Sciences, Krasnoyarsk, Russia

    A. V. Lapko & V. A. Lapko

  2. Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    A. V. Lapko & V. A. Lapko

Authors
  1. A. V. Lapko
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  2. V. A. Lapko
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Correspondence to A. V. Lapko.

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Translated from Izmeritel’naya Tekhnika, No. 5, pp. 3–6, May, 2019.

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Lapko, A.V., Lapko, V.A. Selection of the Blur Coefficient for Probability Density Kernel Estimates Under Conditions of Large Samples. Meas Tech 62, 383–389 (2019). https://doi.org/10.1007/s11018-019-01634-w

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  • DOI: https://doi.org/10.1007/s11018-019-01634-w

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