Hpi(x, nstage=2, pilot="samse", pre="sphere", Hstart, binned=FALSE,
bgridsize, amise=FALSE, deriv.order=0, verbose=FALSE, optim.fun="nlm",
Sdr.flag=FALSE)
Hpi.diag(x, nstage=2, pilot="samse", pre="scale", Hstart, binned=FALSE,
bgridsize, amise=FALSE, deriv.order=0, verbose=FALSE, optim.fun="nlm")
hpi(x, nstage=2, binned=TRUE, bgridsize)
"amse"
= AMSE pilot bandwidths
"samse"
= single SAMSE pilot bandwidth
"unconstr"
= single unconstrained pilot bandwidth
"dscalar"
= single pilot bandwidth for deriv.order > 0
"dunconstr"
"scale"
= pre.scale
, "sphere"
= pre.sphere
amise=TRUE
then the minimal scaled PI value is returned too.hpi
is the univariate plug-in
selector of Wand & Jones (1994), i.e. it is exactly the same as dpik
.
Hpi
is a multivariate generalisation of this. Use Hpi
for full bandwidth matrices and Hpi.diag
for diagonal bandwidth matrices.For AMSE pilot bandwidths, see Wand & Jones (1994). For SAMSE pilot bandwidths, see Duong & Hazelton (2003). The latter is a modification of the former, in order to remove any possible problems with non-positive definiteness. Unconstrained pilot bandwidths are from Chacon & Duong (2010).
For d = 1, 2, 3, 4 and binned=TRUE
,
estimates are computed over a binning grid defined
by bgridsize
. Otherwise it's computed exactly.
If Hstart
is not given then it defaults to Hns(x, deriv.order=r)
.
If Sdr.flag=FALSE
, then symmetriser matrices are not computed
explicitly but indirectly in a loop of permutations. The loop can make
execution times long, but avoid the memory problems for e.g.,
d=4, deriv.order=2.
Hbcv
, Hlscv
, Hscv
data(unicef)
Hpi(unicef, binned=TRUE)
Hpi(unicef, pilot="dscalar")
hpi(unicef[,1])
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