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Maximum Kernel Likelihood Estimation.

Jaki, Thomas and West, R. Webster (2008) Maximum Kernel Likelihood Estimation. Journal of Computational and Graphical Statistics, 17 (4). pp. 976-993. ISSN 1061-8600

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    Abstract

    We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (mkle). A speedy computational method to compute the mkle based on binning is implemented in a simulation study which shows that the mkle at an optimal bandwidth is decidedly superior in terms of efficiency to the sample mean and other measures of location for heavy tailed symmetric distributions. An empirical rule and a computational method to estimate this optimal bandwidth are developed and used to construct bootstrap confidence intervals for the population mean. We show that the intervals have approximately nominal coverage and have significantly smaller average width than the standard t and z intervals. Lastly, we develop some mathematical properties for a very close approximation to the mkle called the kernel mean. In particular, we demonstrate that the kernel mean is indeed unbiased for the population mean for symmetric distributions.

    Item Type: Article
    Journal or Publication Title: Journal of Computational and Graphical Statistics
    Subjects: Q Science > QA Mathematics
    Departments: Faculty of Science and Technology > Mathematics and Statistics
    Faculty of Arts & Social Sciences > Linguistics & English Language
    ID Code: 11512
    Deposited By: Dr Thomas Jaki
    Deposited On: 29 Aug 2008 09:57
    Refereed?: Yes
    Published?: Published
    Last Modified: 09 Oct 2013 14:49
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/11512

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