Multivariate kernel density estimation: Difference between revisions

Revision as of 05:56, 13 April 2020

Revision as of 21:15, 21 May 2020

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The code fragment computes the kernel density estimate with the plug-in bandwidth matrix <math>\hat{\mathbf{H}}_{\operatorname{PI}} = \begin{bmatrix}0.052 & 0.510 \\ 0.510 & 8.882\end{bmatrix}.</math> Again, the coloured contours correspond to the smallest region which contains the respective probability mass: red = 25%, orange + red = 50%, yellow + orange + red = 75%. To compute the SCV selector, <code>Hpi</code> is replaced with <code>Hscv</code>. This is not displayed here since it is mostly similar to the plug-in estimate for this example.

<sourcesyntaxhighlight lang="rsplus" style="overflow:auto;">

library(ks)

data(faithful)

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plot(fhat, display="filled.contour2")

points(faithful, cex=0.5, pch=16)

</syntaxhighlight>

</source>

==Density estimation with a diagonal bandwidth matrix==

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in the current directory.

<sourcesyntaxhighlight lang="matlab" style="overflow:auto;">

clear all

% generate synthetic data

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contour3(X,Y,density,50), hold on

plot(data(:,1),data(:,2),'r.','MarkerSize',5)

</syntaxhighlight>

</source>

==Alternative optimality criteria==