Fast Algorithm for Choosing Blur Coefficients in Multidimensional Kernel Probability Density Estimates


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Аннотация

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.

Авторлар туралы

A. Lapko

Institute of Computational Modeling, Siberian Branch of the Russian Academy of Sciences; Reshetnev Siberian State University of Science and Technology

Хат алмасуға жауапты Автор.
Email: lapko@icm.krasn.ru
Ресей, Krasnoyarsk; Krasnoyarsk

V. 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
Ресей, Krasnoyarsk; Krasnoyarsk

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