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Teor. Veroyatnost. i Primenen., 1983, Volume 28, Issue 4, Pages 691–699 (Mi tvp2217)  

On some nonparametric density function estimators in the statistical classification problem

C. G. Hakubija

Tbilisi

Abstract: In the finite classification problem we construct the asymptotically optimal Bayesian decision procedure base don the Parzen's estimators of the unknown density function. For these estimators it is shown that the using of the variable band width gives some advantages in comparison with the constant one in the sense of the convergence rate of Bayesian risks.

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English version:
Theory of Probability and its Applications, 1984, 28:4, 727–735

Bibliographic databases:

Received: 19.12.1980

Citation: C. G. Hakubija, “On some nonparametric density function estimators in the statistical classification problem”, Teor. Veroyatnost. i Primenen., 28:4 (1983), 691–699; Theory Probab. Appl., 28:4 (1984), 727–735

Citation in format AMSBIB
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\by C.~G.~Hakubija
\paper On some nonparametric density function estimators in the statistical classification problem
\jour Teor. Veroyatnost. i Primenen.
\yr 1983
\vol 28
\issue 4
\pages 691--699
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\mathscinet{http://www.ams.org/mathscinet-getitem?mr=726895}
\zmath{https://zbmath.org/?q=an:0599.62074|0574.62054}
\transl
\jour Theory Probab. Appl.
\yr 1984
\vol 28
\issue 4
\pages 727--735
\crossref{https://doi.org/10.1137/1128071}
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