Email this article Analysis of Optimization Methods for Nonparametric Estimation of the Probability Density with Respect to the Blur Factor of Kernel Functions
- Authors: Lapko A.V.1,2, Lapko V.A.1,2
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Affiliations:
- Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences
- Reshetnev Siberian State University of Science and Technology
- Issue: Vol 60, No 6 (2017)
- Pages: 515-522
- Section: General Problems of Metrology and Measurement Technique
- URL: https://journals.rcsi.science/0543-1972/article/view/246181
- DOI: https://doi.org/10.1007/s11018-017-1228-x
- ID: 246181
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Abstract
The results of a comparison of the most common optimization methods for the nonparametric estimation of the probability density of Rosenblatt–Parzen are presented. To select the optimal values of the blur coefficients of kernel functions, minimum conditions for the standard deviation of the nonparametric estimate of the probability density and the maximum of the likelihood function are used.
About the authors
A. V. Lapko
Institute of Computational Modeling, Siberian Branch, 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, Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
Email: lapko@icm.krasn.ru
Russian Federation, Krasnoyarsk; Krasnoyarsk
