Hns is equal to (4/(n*(d+2*r+2)))^(2/(d+2*r+4))*var(x),
n = sample size, d = dimension of data, r = derivative
order. hns is the analogue of Hns for 1-d data. These
can be used for density (derivative) estimators
kde, kdde.
The equivalents for distribution estimators kcde are
Hns.kcde and hns.cde.
Value
Full normal scale bandwidth matrix.
References
Chacon J.E., Duong, T. & Wand, M.P. (2011). Asymptotics for
general multivariate kernel density derivative
estimators. Statistica Sinica. 21, 807-840.