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

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Measurement Techniques Aims and scope

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.

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

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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

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  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. 6, pp. 16–20, June, 2018.

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Lapko, A.V., Lapko, V.A. Fast Algorithm for Choosing Kernel Function Blur Coefficients in a Nonparametric Probability Density Estimate. Meas Tech 61, 540–545 (2018). https://doi.org/10.1007/s11018-018-1463-9

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  • DOI: https://doi.org/10.1007/s11018-018-1463-9

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