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Symmetric maximum kernel likelihood estimation.

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Symmetric maximum kernel likelihood estimation. / Jaki, Thomas; West, R. Webster.
In: Journal of Statistical Computation and Simulation, Vol. 81, No. 2, 02.2011, p. 193-206.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Jaki, T & West, RW 2011, 'Symmetric maximum kernel likelihood estimation.', Journal of Statistical Computation and Simulation, vol. 81, no. 2, pp. 193-206. https://doi.org/10.1080/00949650903232664

APA

Jaki, T., & West, R. W. (2011). Symmetric maximum kernel likelihood estimation. Journal of Statistical Computation and Simulation, 81(2), 193-206. https://doi.org/10.1080/00949650903232664

Vancouver

Jaki T, West RW. Symmetric maximum kernel likelihood estimation. Journal of Statistical Computation and Simulation. 2011 Feb;81(2):193-206. doi: 10.1080/00949650903232664

Author

Jaki, Thomas ; West, R. Webster. / Symmetric maximum kernel likelihood estimation. In: Journal of Statistical Computation and Simulation. 2011 ; Vol. 81, No. 2. pp. 193-206.

Bibtex

@article{ad9a902104304b6093d1b910d0674d3e,
title = "Symmetric maximum kernel likelihood estimation.",
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.",
keywords = "bandwidth, binning, kernel density estimation, maximum likelihood estimation, symmetric density",
author = "Thomas Jaki and West, {R. Webster}",
year = "2011",
month = feb,
doi = "10.1080/00949650903232664",
language = "English",
volume = "81",
pages = "193--206",
journal = "Journal of Statistical Computation and Simulation",
issn = "1563-5163",
publisher = "Taylor and Francis Ltd.",
number = "2",

}

RIS

TY - JOUR

T1 - Symmetric maximum kernel likelihood estimation.

AU - Jaki, Thomas

AU - West, R. Webster

PY - 2011/2

Y1 - 2011/2

N2 - 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.

AB - 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.

KW - bandwidth

KW - binning

KW - kernel density estimation

KW - maximum likelihood estimation

KW - symmetric density

U2 - 10.1080/00949650903232664

DO - 10.1080/00949650903232664

M3 - Journal article

VL - 81

SP - 193

EP - 206

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

SN - 1563-5163

IS - 2

ER -