klik: Kernel log likelihood

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

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

Usage

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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

mkle

Examples

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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)

MKLE documentation built on May 1, 2019, 6:49 p.m.