ks (version 1.4.2)

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)

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. Both of these pilot bandwidths require numerical optimisation.

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, nstage=1, pilot="amse", pre="scale")
Hpi(unicef, nstage=2, pilot="samse", pre="sphere")
Hpi.diag(unicef, nstage=2, pilot="amse", pre="scale") 

### large sample - 10000 sample from bivariate standard normal 

x <- rmvnorm.mixt(10000, c(0,0), diag(2))    
Hpi(x, binned=TRUE)

### trivariate example

mus <- rbind(c(0,0,0), c(2,2,2))
Sigma <- matrix(c(1, 0.7, 0.7, 0.7, 1, 0.7, 0.7, 0.7, 1), nr=3, nc=3) 
Sigmas <- rbind(Sigma, Sigma)
x <- rmvnorm.mixt(n=100, mus=mus, Sigmas=Sigmas, props=c(1/2, 1/2))
Hpi(x)