kfe: Kernel functional estimate

Description

Kernel functional estimate for 1- to 6-dimensional data.

Usage

kfe(x, G, deriv.order, inc=1, binned=FALSE, bin.par, bgridsize, deriv.vec=TRUE,
    add.index=TRUE, verbose=FALSE)
Hpi.kfe(x, nstage=2, pilot, pre="sphere", Hstart, binned=FALSE, 
    bgridsize, amise=FALSE, deriv.order=0, verbose=FALSE, optim.fun="nlm")
Hpi.diag.kfe(x, nstage=2, pilot, pre="scale", Hstart, binned=FALSE,
    bgridsize, amise=FALSE, deriv.order=0, verbose=FALSE, optim.fun="nlm")
hpi.kfe(x, nstage=2, binned=FALSE, bgridsize, amise=FALSE, deriv.order=0)

Arguments

vector/matrix of data values

nstage

number of stages in the plug-in bandwidth selector (1 or 2)

pilot

"dscalar" = single pilot bandwidth (default) "dunconstr" = single unconstrained pilot bandwidth

pre

"scale" = pre.scale, "sphere" = pre.sphere

Hstart

initial bandwidth matrix, used in numerical optimisation

binned

flag for binned estimation. Default is FALSE.

bgridsize

vector of binning grid sizes

amise

flag to return the minimal scaled PI value

deriv.order

derivative order

verbose

flag to print out progress information. Default is FALSE.

optim.fun

optimiser function: one of nlm or optim

pilot bandwidth matrix

inc

0=exclude diagonal, 1=include diagonal terms in kfe calculation

bin.par

binning parameters - output from binning

deriv.vec

flag to compute duplicated partial derivatives in the vectorised form. Default is FALSE.

add.index

flag to ouput derivative indices matrix. Default is true.

Value

Plug-in bandwidth matrix for $r$-th order kernel functional estimator.

Details

Hpi.kfe is the optimal plug-in bandwidth for $r$-th order kernel functional estimator based on the unconstrained pilot selectors of Chacon & Duong (2010). hpi.kfe is the 1-d equivalent, using the formulas from Wand & Jones (1995, p.70).

kfe does not usually need to be called explicitly by the user.

References