Email this article Fast Algorithm for Choosing Kernel Function Blur Coefficients in a Nonparametric Probability Density Estimate
- Authors: Lapko A.V.1,2, Lapko V.A.1,2
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Affiliations:
- Institute of Computational Modeling, Siberian Branch of the Russian Academy of Sciences
- Reshetnev Siberian State University of Science and Technology
- Issue: Vol 61, No 6 (2018)
- Pages: 540-545
- Section: Article
- URL: https://journals.rcsi.science/0543-1972/article/view/246502
- DOI: https://doi.org/10.1007/s11018-018-1463-9
- ID: 246502
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Abstract
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.
About the authors
A. V. Lapko
Institute of Computational Modeling, Siberian Branch of the Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
Author for correspondence.
Email: lapko@icm.krasn.ru
Russian Federation, Krasnoyarsk; Krasnoyarsk
V. A. Lapko
Institute of Computational Modeling, Siberian Branch of the Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
Email: lapko@icm.krasn.ru
Russian Federation, Krasnoyarsk; Krasnoyarsk
