Improved Maximum Likelihood Estimation of the Shape Parameter in the Nakagami Distribution

Jacob Schwartz, Ryan T. Godwin () and David Giles

No 1109, Econometrics Working Papers from Department of Economics, University of Victoria

Abstract: We develop and evaluate analytic and bootstrap bias-corrected maximum likelihood estimators for the shape parameter in the Nakagami distribution. This distribution is widely used in a variety of disciplines, and the corresponding estimator of its scale parameter is trivially unbiased. We find that both “corrective” and “preventive” analytic approaches to eliminating the bias, to O(n-2), are equally, and extremely, effective and simple to implement. As a bonus, the sizeable reduction in bias comes with a small reduction in mean squared error. Overall, we prefer analytic bias corrections in the case of this estimator. This preference is based on the relative computational costs and the magnitudes of the bias reductions that can be achieved in each case. Our results are illustrated with two real-data applications, including one which provides the first application of the Nakagami distribution to data for ocean wave heights.

Keywords: Nakagami distribution; maximum likelihood estimation; bias reduction (search for similar items in EconPapers)
JEL-codes: C13 C15 C16 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2011-05-18
Note: ISSN 1485-6441
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:1109

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