klik {MKLE} | R Documentation |
Kernel log likelihood
Description
The function computes the kernel log likelihood for a given \hat \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. (2008) Maximum kernel likelihood estimation. Journal of Computational and Graphical Statistics Vol. 17(No 4), 976-993.
See Also
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 1.0.1 Index]