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Kernel and pseudokernel estimators for the a priori density of a multivariate parameter

✍ Scribed by M. Ya. Penskaya

Book ID
118292270
Publisher
Springer US
Year
1998
Tongue
English
Weight
379 KB
Volume
88
Category
Article
ISSN
1573-8795

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πŸ“œ SIMILAR VOLUMES

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In this paper we consider the weighted average square error A,(rc)= (l/n)~=1 {f"(3))f(Xj)}2~(Xj), where f is the common density function of the independent and identically distributed random vectors X~ ..... X,, f, is the kernel estimator based on these vectors and ~z is a weight function. Using U-s

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The computational cost of multivariate kernel density estimation can be reduced by prebinning the data. The data are discretized to a grid and a weighted kernel estimator is computed. We report results on the accuracy of such a binned kernel estimator and discuss the computational complexity of the