ks (version 1.5.5)

Hpi, Hpi.diag: Plug-in bandwidth matrix selector for multivariate data

Description

Plug-in bandwidth matrix for for 2- to 6-dimensional data.

Usage

Hpi(x, nstage=2, pilot="samse", pre="sphere", Hstart,
    binned=FALSE, bgridsize)
Hpi.diag(x, nstage=2, pilot="amse", pre="scale", Hstart,
    binned=FALSE, bgridsize)

Arguments

x

matrix of data values

nstage

number of stages in the plug-in bandwidth selector (1 or 2)

pilot

"amse" = AMSE pilot bandwidths, "samse" = single SAMSE pilot bandwidth

pre

"scale" = pre-scaling, "sphere" = pre-sphering

Hstart

initial bandwidth matrix, used in numerical optimisation

binned

flag for binned kernel estimation

bgridsize

vector of binning grid sizes - required only if binned=TRUE

Value

• Plug-in bandwidth matrix.

Details

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.

For d = 1, 2, 3, 4 and binned=TRUE, the density estimate is computed over a binning grid defined by bgridsize. Otherwise it's computed exactly. For details on the pre-transformations in pre, see pre.sphere and pre.scale.

If Hstart is not given then it defaults to k*var(x) where k = $\left[\frac{4}{n(d+2)}\right]^{2/(d+4)}$, n = sample size, d = dimension of data.

References

Wand, M.P. & Jones, M.C. (1994) Multivariate plugin bandwidth selection. Computational Statistics, 9, 97-116. Duong, T. & Hazelton, M.L. (2003) Plug-in bandwidth matrices for bivariate kernel density estimation. Journal of Nonparametric Statistics, 15, 17-30.

Examples

## bivariate example
data(unicef)
Hpi(unicef)
Hpi(unicef, binned=TRUE)
Hpi.diag(unicef)

## 4-variate example
x <- rmvnorm.mixt(n=100, mus=rep(0,4), diag(4))
Hpi(x)
Hpi.diag(x, pilot="samse")