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Hscv(x, pre="sphere", Hstart, binned=FALSE, bgridsize)
Hscv.diag(x, pre="scale", Hstart, binned=FALSE, bgridsize)
"scale"
= pre-scaling, "sphere"
= pre-spheringbinned=TRUE
For d = 1, 2, 3, 4 and binned=TRUE
,
the density estimate is computed over a binning grid defined
by bgridsize
. Otherwise it's computed exactly.
For details on the pre-transformations in pre
, see
pre.sphere
and pre.scale
.
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.
Duong, T. & Hazelton, M.L. (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinavian Journal of Statistics. 32, 485-506.
Hlscv
, Hbcv
data(unicef)
Hscv(unicef)
Hscv(unicef, binned=TRUE)
Hscv.diag(unicef)
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