initial bandwidth matrix, used in numerical
optimisation
binned
flag for binned kernel estimation. Default is FALSE.
bgridsize
vector of binning grid sizes
amise
flag to return the minimal LSCV value. Default is FALSE.
deriv.order
derivative order
verbose
flag to print out progress information. Default is FALSE.
optim.fun
optimiser function: one of nlm or optim
trunc
parameter to control truncation for numerical
optimisation. Default is 4 for density.deriv>0, otherwise no
truncation. For details see below.
...
parameters as above
Details
hlscv is the univariate LSCV
selector of Bowman (1984) and Rudemo (1982). Hlscv is a
multivariate generalisation of this. Use Hlscv for full
bandwidth matrices and Hlscv.diag for diagonal bandwidth matrices.
Hucv, Hucv.diag and hucv are aliases with UCV
unbiased cross validation instead of LSCV.
Truncation of the parameter space is usually required for the LSCV selector,
for r > 0, to find a reasonable solution to the numerical optimisation.
If a candidate matrix H is
such that det(H) is not in [1/trunc, trunc]*det(H0) or
abs(LSCV(H)) > trunc*abs(LSCV0) then the LSCV(H) is reset to LSCV0 where
H0=Hns(x) and LSCV0=LSCV(H0).
For details about the advanced options for binned,Hstart,
see Hpi.
Value
LSCV bandwidth. If amise=TRUE then the minimal LSCV value is returned too.
References
Bowman, A. (1984) An alternative method of cross-validation for the
smoothing of kernel density estimates. Biometrika. 71,
353-360.
Rudemo, M. (1982) Empirical choice of histograms and kernel density
estimators. Scandinavian Journal of Statistics. 9,
65-78.