# klik: Kernel log likelihood In MKLE: Maximum kernel likelihood estimation.

## Description

The function computes the kernel log likelihood for a given \hat θ .

## Usage

 1 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.

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.

mkle
  1 2 3 4 5 6 7 8 9 10 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)