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CONVERGENCE PROPERTIES OF MULTI-DIMENSIONAL NEAREST NEIGHBOR DENSITY ESTIMATES FOR THE CASE OF GENERAL KERNEL

机译:多维最近邻密度估计的收敛性属性常规内核的情况

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摘要

In this paper we give weaker conditions to ensure the strong uniform consis-tency of multi-dimensional nearest neighbor (N.N.) estimates with non-uniform kernel andobtain the convergence rates of these estimates on an arbitrary bounded set. The ratescan not be improved in some sense. Obviously, the problem of strong convergence rates ata given point is its special case. The range of applications of estimates is extended.
机译:在本文中,我们提供较弱的条件,以确保使用非统一内核的多维最近邻(N.N.)估计的强大统一均匀链接性。这些估计的随意限定集合的收敛速率。在某种意义上没有改善卢比。显然,特定点的强烈收敛速率问题是其特殊情况。估计的应用范围延长。

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