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An Unconventional Technique for Choosing the Kernel Function Blur Coefficients in Nonparametric Regression

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

The traditional method for choosing the kernel function blur coefficients in nonparametric regression is based on minimizing the root mean square error in the approximation of the desired dependence on the initial statistical data. With an increase in the volume of the training sample of the values of the variables of the restored dependence, the computational costs in optimizing nonparametric regression increase significantly. An unconventional method is proposed for choosing the blur coefficients of nonparametric regression that are optimal for the kernel probability densities of the variables of the reconstructed dependence. Statistical estimates of the root mean square deviations of the joint probability density of the variables of the reconstructed dependence were used as an optimality criterion in choosing the blur coefficients of the kernel probability densities. The proposed technique made it possible to avoid the calculation of the approximation error of the restored dependence by nonparametric regression, which was confirmed by the results of computational experiments. The results obtained make it possible to use the method of fast optimization of kernel estimates of probability densities in the synthesis of nonparametric regression.

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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. 2, pp. 3–7, February, 2022.

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Lapko, A.V., Lapko, V.A. An Unconventional Technique for Choosing the Kernel Function Blur Coefficients in Nonparametric Regression. Meas Tech 65, 83–88 (2022). https://doi.org/10.1007/s11018-022-02053-0

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  • DOI: https://doi.org/10.1007/s11018-022-02053-0

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