klik {MKLE}R Documentation

Kernel log likelihood

Description

The function computes the kernel log likelihood for a given $theta$.

Usage

klik(delta , data, kde, grid, min)

Arguments

delta the difference of the parameter $theta$ for which the kernel log likelihood will be computed and the sample mean.
data the data for which the kernel log likelihood will be computed.
kde an object of the class "density".
grid the stepsize between the x-values in kde.
min the smallest x-value in kde.

Details

This function is intended to be called through the function mkle and is optimized for fast computation.

Value

The log likelihood based on the shifted kernel density estimator.

Author(s)

Thomas Jaki

References

Jaki T., West R. W. (2007) Maximum kernel likelihood estimation. Submitted to textit{Journal of Computational and Graphical Statistics}.

See Also

mkle

Examples

data(state)
attach(state)
bw<-2*sd(CRIME)
kdensity<-density(CRIME,bw=bw,kernel="biweight",
          from=min(CRIME)-2*bw,to=max(CRIME)+2*bw,n=2^12)
min<-kdensity$x[1]
grid<-kdensity$x[2]-min

# finds the kernel log likelihood at the sample mean
klik(0,CRIME, kdensity, grid, min)


[Package MKLE version 0.04 Index]