Description Details Author(s) References Examples

Computes the maximum kernel likelihood estimator using fast fourier transforms.

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*.

Thomas Jaki

Maintainer: Thomas Jaki <[email protected]>

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.

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