MKLE-package: Maximum kernel likelihood estimation

In MKLE: Maximum Kernel Likelihood Estimation

Maximum kernel likelihood estimation

Description

Computes the maximum kernel likelihood estimator using fast fourier transforms.

Details

Package:	MKLE
Type:	Package
Version:	1.01
Date:	2023-08-21
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,\dots,X_n), and is defined as

\hat L(\theta|X_1,\dots,X_n) = \prod_{i=1}^n \hat f(X_{i}-(\bar{X}-\theta)|h,X_1,\dots,X_n).

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

Author(s)

Thomas Jaki

Maintainer: Thomas Jaki <[e-mail]>

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)

MKLE documentation built on Aug. 21, 2023, 9:09 a.m.