ks (version 1.8.2)

Hlscv: Least-squares cross-validation (LSCV) bandwidth matrix selector for multivariate data

Description

LSCV bandwidth for 1- to 6-dimensional data

Usage

Hlscv(x, Hstart, binned=FALSE, bgridsize, amise=FALSE, kfold=1, 
      deriv.order=0, verbose=FALSE, optim.fun="nlm")
Hlscv.diag(x, Hstart, binned=FALSE, bgridsize, amise=FALSE, kfold=1, 
      deriv.order=0, verbose=FALSE, optim.fun="nlm")
hlscv(x, binned=TRUE, bgridsize, deriv.order=0)

Arguments

x

vector or matrix of data values

Hstart

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.

kfold

value for k-fold bandwidth selection. See details below.

deriv.order

derivative order

verbose

flag to print out progress information. Default is FALSE.

optim.fun

optimiser function: one of nlm or optim.

Value

• LSCV bandwidth. If amise=TRUE then the minimal LSCV value is returned too.

Details

hlscv is the univariate SCV 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.

For details about the advanced options for binned,Hstart,kfold, see Hpi.

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.

See Also

Hbcv, Hscv

Examples

library(MASS)
data(forbes)
Hlscv(forbes)
Hlscv.diag(forbes, binned=TRUE)
hlscv(forbes$bp)