Kernel-based methods are most popular for non-parametric estimators
They can discover structural features in data which parametric approaches may not reveal
Kernel performance is measured by AMISE (asymptotic mean integrated squared error)
Below graphs were produced via Epanechnikov kernel, since Epanechnikov kernel minimizes AMISE
For below graphs we compute a probability density estimate of a sample vector and then plot the evaluation of this probability density estimate
Plots (lines) below correspond to probability density function for k=16 from Cocomo81 and Cocomo81 itself respectively. The stars are the sorted effort values of related datasets.
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