quantreg (version 3.03)

akj: Density estimation using adaptive kernel method

Description

univariate adaptive kernel density estimation a la Silverman

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

Silverman, B. (1986) Density Estimation, pp100-104.