Hbcv(x, whichbcv=1, Hstart, kfold=1)
Hbcv.diag(x, whichbcv=1, Hstart, kfold=1)
Hbcv
for full bandwidth matrices and Hbcv.diag
for diagonal bandwidth matrices. These selectors are only
available for bivariate data.There are two types of BCV criteria considered here. They are known as BCV1 and BCV2, from Sain, Baggerly & Scott (1994) and they only differ slightly. These BCV surfaces can have multiple minima and so it can be quite difficult to locate the most appropriate minimum.
If Hstart
is not given then it defaults to
k*var(x)
where k = $\left[\frac{4}{n(d+2)}\right]^{2/(d+4)}$, n = sample size, d = dimension of data.
For large samples, k-fold bandwidth selection can significantly reduce computation time. The full data sample is partitioned into k sub-samples and a bandwidth matrix is computed for each of these sub-samples. The bandwidths are averaged and re-weighted to serve as a proxy for the full sample selector.
Hlscv
, Hscv
data(unicef)
Hbcv(unicef)
Hbcv.diag(unicef)
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