Hscv(x, pre="sphere", pilot="samse", Hstart, binned=TRUE, bgridsize,
amise=FALSE, kfold=1)
Hscv.diag(x, pre="scale", Hstart, binned=FALSE, bgridsize,
amise=FALSE, kfold=1)
hscv(x, nstage=2, binned=TRUE, bgridsize, plot=FALSE)"scale" = pre-scaling, "sphere" = pre-sphering"amse" = AMSE pilot bandwidths,
"samse" = single SAMSE pilot bandwidth,
"unconstr" = unconstrained pilot bandwidth matrixamise=TRUE then the minimal scaled SCV value is returned too.hsv is the univariate SCV
selector of Jones, Marron & Park (1991). Hscv is a
multivariate generalisation of this, see Duong & Hazelton (2005).
Use Hscv for full bandwidth matrices and Hscv.diag
for diagonal bandwidth matrices. For d = 1, the selector hscv is not always stable for large
sample sizes with binning.
Examine the plot from hscv(, plot=TRUE) to
determine the appropriate smoothness of the SCV function. Any
non-smoothness is due to the discretised nature of binned estimation.
For details about the advanced options for amise, binned, Hstart, kfold,
see Hpi.
Duong, T. & Hazelton, M.L. (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinavian Journal of Statistics. 32, 485-506.
Hlscv, Hbcv, Hpidata(unicef)
Hscv(unicef)
Hscv.diag(unicef, binned=TRUE)
hscv(unicef[,1])Run the code above in your browser using DataLab