On some nonparametric density function estimators in the statistical classification problem

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.