# MKLE-package: Maximum kernel likelihood estimation In MKLE: Maximum kernel likelihood estimation.

## Description

Computes the maximum kernel likelihood estimator using fast fourier transforms.

## Details

 Package: MKLE Type: Package Version: 0.05 Date: 2008-05-02 License: GPL

The maximum kernel likelihood estimator is defined to be the value \hat θ that maximizes the estimated kernel likelihood based on the general location model,

f(x|θ) = f_{0}(x - θ).

This model assumes that the mean associated with $f_0$ is zero which of course implies that the mean of X_i is θ. The kernel likelihood is the estimated likelihood based on the above model using a kernel density estimate, \hat f(.|h,X_1,…,X_n), and is defined as

\hat L(θ|X_1,…,X_n) = ∏_{i=1}^n \hat f(X_{i}-(\bar{X}-θ)|h,X_1,…,X_n).

The resulting estimator therefore is an estimator of the mean of X_i.

## Author(s)

Thomas Jaki

Maintainer: Thomas Jaki <[email protected]>

## References

Jaki T., West R. W. (2008) Maximum kernel likelihood estimation. Journal of Computational and Graphical Statistics Vol. 17(No 4), 976-993.

Silverman, B. W. (1986), Density Estimation for Statistics and Data Analysis, Chapman & Hall, 2nd ed.

## Examples

 1 2 data(state) mkle(state\$CRIME) 

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