Hlscv, Hlscv.diag {ks}R Documentation

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

Description

LSCV bandwidth matrix for 2- to 6-dimensional data

Usage

Hlscv(x, Hstart)
Hlscv.diag(x, Hstart)

Arguments

x matrix of data values
Hstart initial bandwidth matrix, used in numerical optimisation

Details

Use Hlscv for full bandwidth matrices and Hlscv.diag for diagonal bandwidth matrices.

If Hstart is not given then it defaults to k*var(x) where k = 4/(n*(d + 2))^(2/(d+ 4)), n = sample size, d = dimension of data.

Value

LSCV bandwidth matrix.

References

Sain, S.R, Baggerly, K.A & Scott, D.W. (1994) Cross-validation of multivariate densities. Journal of the American Statistical Association. 82, 1131-1146.

Duong, T. & Hazelton, M.L. (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinavian Journal of Statistics. 32, 485-506.

See Also

Hbcv, Hscv

Examples

mus <- rbind(c(-3/2,0), c(3/2,0))
Sigmas <- rbind(diag(c(1/16, 1)), rbind(c(1/16, 1/18), c(1/18, 1/16)))
props <- c(2/3, 1/3)
x <- rmvnorm.mixt(100, mus, Sigmas, props)
Hlscv(x)
Hlscv.diag(x)

[Package ks version 1.4.2 Index]