Jaki, Thomas and West, R. Webster (2011) Symmetric maximum kernel likelihood estimation. Journal of Statistical Computation and Simulation, 81 (2). pp. 193-206.Full text not available from this repository.
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.
|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
|Deposited By:||Dr Thomas Jaki|
|Deposited On:||07 Oct 2009 10:12|
|Last Modified:||07 Jan 2015 14:05|
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