Fast Algorithm for Choosing Kernel Function Blur Coefficients in a Nonparametric Probability Density Estimate
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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.
Keywordsnonparametric estimation of probability density choice of blurring coefficients estimation of mean square deviation of probability density estimation
This study was supported by the Russian Foundation for Basic Research (Grant No. 18-01-00251).
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