Email this article Fast Algorithm for Choosing Blur Coefficients in Multidimensional Kernel Probability Density Estimates
- 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 10 (2019)
- Pages: 979-986
- Section: Article
- URL: https://journals.rcsi.science/0543-1972/article/view/246616
- DOI: https://doi.org/10.1007/s11018-019-01536-x
- ID: 246616
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Abstract
A method is proposed for quickly choosing the blur coefficients of kernel functions in a non-parametric estimate of a multidimensional probability density of Rosenblatt–Parzen type. The technique is based on the analysis of the asymptotic properties of a multidimensional probability density estimate. The properties of the fast algorithm for choosing the blur coefficients of a kernel probability density estimate are investigated.
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
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