Hscv {ks} | R Documentation |
Smoothed cross-validation (SCV) bandwidth selector
Description
SCV bandwidth for 1- to 6-dimensional data.
Usage
Hscv(x, nstage=2, pre="sphere", pilot, Hstart, binned,
bgridsize, amise=FALSE, deriv.order=0, verbose=FALSE, optim.fun="optim")
Hscv.diag(x, nstage=2, pre="scale", pilot, Hstart, binned,
bgridsize, amise=FALSE, deriv.order=0, verbose=FALSE, optim.fun="optim")
hscv(x, nstage=2, binned=TRUE, bgridsize, plot=FALSE)
Arguments
x |
vector or matrix of data values |
pre |
"scale" = |
pilot |
"amse" = AMSE pilot bandwidths |
Hstart |
initial bandwidth matrix, used in numerical optimisation |
binned |
flag for binned kernel estimation |
bgridsize |
vector of binning grid sizes |
amise |
flag to return the minimal scaled SCV value. Default is FALSE. |
deriv.order |
derivative order |
verbose |
flag to print out progress information. Default is FALSE. |
optim.fun |
optimiser function: one of |
nstage |
number of stages in the SCV bandwidth selector (1 or 2) |
plot |
flag to display plot of SCV(h) vs h (1-d only). Default is FALSE. |
Details
hscv
is the univariate SCV
selector of Jones, Marron & Park (1991). Hscv
is a
multivariate generalisation of this, see Duong & Hazelton (2005).
Use Hscv
for unconstrained bandwidth matrices and Hscv.diag
for diagonal bandwidth matrices.
The default pilot is "samse"
for d=2, r=0, and
"dscalar"
otherwise. For SAMSE pilot bandwidths, see Duong &
Hazelton (2005). Unconstrained and higher order derivative pilot
bandwidths are from Chacon & Duong (2011).
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 binned, Hstart, optim.fun
,
see Hpi
.
Value
SCV bandwidth. If amise=TRUE
then the minimal scaled SCV value is returned too.
References
Chacon, J.E. & Duong, T. (2011) Unconstrained pilot selectors for smoothed cross validation. Australian & New Zealand Journal of Statistics, 53, 331-351.
Duong, T. & Hazelton, M.L. (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinavian Journal of Statistics, 32, 485-506.
Jones, M.C., Marron, J.S. & Park, B.U. (1991) A simple root n
bandwidth selector. Annals of Statistics, 19, 1919-1932.
See Also
Examples
data(unicef)
Hscv(unicef)
hscv(unicef[,1])