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Home  >  Volume 28. No.2 (Nov. 2014)

30. A Brief Review of Kernel Density Estimation with Applications to Data Analysis by Siloko I.U. and Ishiekwene C.C. - Volume28, No. 2, (November, 2014), pp 195 – 206
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Abstract 

Kernel density estimation is an important smoothingtechnique with direct applications such as exploratory data analysis and  data visualisation.This review summarizes the most important theoretical aspects of kernel density estimation and provides a description of classical methods for computing the smoothing parameter. The performance of the kernel estimator will be considered based on the classical methods of obtaining the smoothing parameter and this will be fully illustrated by real data examples. Key words:Smoothing parameter, Bias, Variance, Unbiased Cross validation, Biased Cross validation, Smoothed Cross validation, Smoothed Bootstrap, Asymptotic Mean Integration Squared Error (AMISE).. 

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