Hmise.mixt, Hamise.mixt {ks} | R Documentation |
For normal mixture densities, we have a closed form for the MISE and AMISE. So in these cases, we can numerically minimise these criteria to find MISE- and AMISE-optimal matrices.
Hmise.mixt(mus, Sigmas, props, samp, Hstart) Hamise.mixt(mus, Sigmas, props, samp, Hstart)
mus |
(stacked) matrix of mean vectors |
Sigmas |
(stacked) matrix of variance matrices |
props |
vector of mixing proportions |
samp |
sample size |
Hstart |
initial bandwidth matrix, used in numerical optimisation |
For normal mixture densities, the MISE and AMISE have exact formulas. See Wand & Jones (1995).
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.
Full MISE- or AMISE-optimal bandwidth matrix. Please note that diagonal forms of these matrices are not available.
Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall. London.
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) samp <- 100 Hmise.mixt(mus, Sigmas, props, samp) Hamise.mixt(mus, Sigmas, props, samp)