Di Marzio, M, Fensore, S, Panzera, A et al. (1 more author) (2017) Nonparametric estimating equations for circular probability density functions and their derivatives. Electronic Journal of Statistics, 11 (2). pp. 4323-4346. ISSN 1935-7524
Abstract
We propose estimating equations whose unknown parameters are the values taken by a circular density and its derivatives at a point. Specifically, we solve equations which relate local versions of population trigonometric moments with their sample counterparts. Major advantages of our approach are: higher order bias without asymptotic variance inflation, closed form for the estimators, and absence of numerical tasks. We also investigate situations where the observed data are dependent. Theoretical results along with simulation experiments are provided.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This article is open access article under the terms of the Creative Commons Attribution 4.0 International License. |
Keywords: | Circular kernels, density estimation, Fourier coefficients, jackknife, sin-polynomials, trigonometric moments, von Mises density |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Nov 2017 11:23 |
Last Modified: | 23 Jun 2023 22:39 |
Status: | Published |
Publisher: | Institute of Mathematical Statistics |
Identification Number: | 10.1214/17-EJS1318 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:123646 |