Отправить статью по E-mail Analysis of Optimization Methods for Nonparametric Estimation of the Probability Density with Respect to the Blur Factor of Kernel Functions
- Авторы: Lapko A.1,2, Lapko V.1,2
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Учреждения:
- Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences
- Reshetnev Siberian State University of Science and Technology
- Выпуск: Том 60, № 6 (2017)
- Страницы: 515-522
- Раздел: 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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Аннотация
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.
Об авторах
A. Lapko
Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
Автор, ответственный за переписку.
Email: lapko@icm.krasn.ru
Россия, Krasnoyarsk; Krasnoyarsk
V. Lapko
Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology
Email: lapko@icm.krasn.ru
Россия, Krasnoyarsk; Krasnoyarsk
