akj {quantreg}R Documentation

Density estimation using adaptive kernel method

Description

univariate adaptive kernel density estimation a la Silverman. As used by Portnoy and Koenker (1989)

Usage

akj(x, z, p, h, alpha, kappa, iker1, iker2)

Arguments

x points used for centers of kernel assumed to be sorted
z points at which density is calculated; default to seq( min(x), max(x), 2*length(x) )
p vector of probabilities associated with x's; default to 1/len(x) for each x.
h initial window size (overall); default to Silverman's normal reference
alpha a sensitivity parameter that determines the sensitivity of the local bandwidth to variations in the pilot density; default to .5
kappa constant determining initial (default) window width
iker1 kernel indicator, 0 for normal kernel (default) while 1 for cauchy kernel
iker2 xxx

Value

a R structure is returned

dens the vector of estimated density
psi a vector of psi=-f'/f function
score a vector of score (f'/f)^2-f''/f function
h same as the input argument h

References

Portnoy, S and R Koenker, (1989) Adaptive L Estimation of Linear Models, Annals, 17, 362-81. Silverman, B. (1986) Density Estimation, pp100-104.


[Package quantreg version 3.82 Index]