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Symmetric maximum kernel likelihood estimation.

Jaki, Thomas and West, R. Webster (2011) Symmetric maximum kernel likelihood estimation. Journal of Statistical Computation and Simulation, 81 (2). pp. 193-206.

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Abstract

We introduce an estimator for the population mean based on maximizing likelihoods formed from a symmetric kernel density estimate. Due to these origins, we have dubbed the estimator the symmetric maximum kernel likelihood estimate (smkle). A speedy computational method to compute the smkle based on binning is implemented in a simulation study which shows that the smkle 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 corresponding intervals for other measures of location.

Item Type: Article
Journal or Publication Title: Journal of Statistical Computation and Simulation
Uncontrolled Keywords: bandwidth ; binning ; kernel density estimation ; maximum likelihood estimation ; symmetric density
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
Faculty of Arts & Social Sciences > Linguistics & English Language
ID Code: 27168
Deposited By: Dr Thomas Jaki
Deposited On: 07 Oct 2009 10:12
Refereed?: Yes
Published?: Published
Last Modified: 09 Oct 2013 14:50
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/27168

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