Hscv(x, pre="sphere", pilot="samse", Hstart, binned=TRUE, bgridsize,
amise=FALSE, kfold=1)
Hscv.diag(x, pre="scale", Hstart, binned=FALSE, bgridsize,
amise=FALSE, kfold=1)
hscv(x, nstage=2, binned=TRUE, bgridsize, plot=FALSE)
"scale"
= pre-scaling, "sphere"
= pre-sphering"amse"
= AMSE pilot bandwidths,
"samse"
= single SAMSE pilot bandwidth,
"unconstr"
= unconstrained pilot bandwidth matrixamise=TRUE
then the minimal scaled SCV value is returned too.hsv
is the univariate SCV
selector of Jones, Marron & Park (1991). Hscv
is a
multivariate generalisation of this, see Duong & Hazelton (2005).
Use Hscv
for full bandwidth matrices and Hscv.diag
for diagonal bandwidth matrices. For d = 1, the selector hscv
is not always stable for large
sample sizes with binning.
Examine the plot from hscv(, plot=TRUE)
to
determine the appropriate smoothness of the SCV function. Any
non-smoothness is due to the discretised nature of binned estimation.
For details about the advanced options for amise, binned, Hstart, kfold
,
see Hpi
.
Duong, T. & Hazelton, M.L. (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinavian Journal of Statistics. 32, 485-506.
Hlscv
, Hbcv
, Hpi
data(unicef)
Hscv(unicef)
Hscv.diag(unicef, binned=TRUE)
hscv(unicef[,1])
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