MKLE-package {MKLE}R Documentation

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 theta that maximizes the estimated kernel likelihood based on the general location model,

f(x|theta) = f_{0}(x - theta).

This model assumes that the mean associated with $f_0$ is zero which of course implies that the mean of X_i is theta. 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(theta|X_1,...,X_n) = prod_{i=1}^n hat f(X_{i}-(bar{X}-theta)|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 <jaki.thomas@gmail.com>

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

data(state)
mkle(state$CRIME)

[Package MKLE version 0.05 Index]