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Fast algorithm bandwidth selection for multivariate kernel density estimation Fast algorithm bandwidth selection for multivariate kernel density estimation
https://doi.org/10.32446/0368-1025it.2018-10-19-23
Abstract
Proposed fast technique bandwidth selection for kernel function in multivariate nonparametric density estimation of the Rosenblatt-Parzen type. The method is based on the results of an analysis of the asymptotic properties of the multivariate density estimate. The properties of a fast algorithm bandwidth selection for multivariate kernel density estimation are investigated.
Keywords
непараметрическая оценка многомерной плотности вероятности,
выбор коэффициентов размытости,
оценка Розенблатта-Парзена,
быстрый алгоритм оптимизации,
асимптотические свойства,
многомерный анализ данных,
multivariate nonparametric density estimation,
bandwidth selection,
Rosenblatt-Parzen estimate,
fast optimization algorithm,
asymptotic properties,
multivariate data analysis
About the Authors
A. V. Lapko
Institute of Computational Modeling, Siberian Branch of the Russian Academy of Sciences (ICM SB RAS)
Russian Federation
Russian Federation
V. A. Lapko
Reshetnev Siberian State University of Science and Technology
Russian Federation
Russian Federation
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Review
For citations:
Lapko A.V., Lapko V.A. Fast algorithm bandwidth selection for multivariate kernel density estimation Fast algorithm bandwidth selection for multivariate kernel density estimation. Izmeritel`naya Tekhnika. 2018;(10):19-23. (In Russ.) https://doi.org/10.32446/0368-1025it.2018-10-19-23
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